multidimensional heterogeneous medical data push in

Research ArticleMultidimensional Heterogeneous Medical Data Push inIntelligent Cloud Collaborative Management System

Gang Liu1 and Xiaofeng Li 2

1College of Computer Science and Technology Harbin Engineering University Harbin 150001 China2Department of Information Engineering Heilongjiang International University Harbin 150025 China

Correspondence should be addressed to Xiaofeng Li lixiaofenghiunetcn

Received 5 July 2020 Revised 3 October 2020 Accepted 7 October 2020 Published 19 October 2020

Academic Editor Abd E I-Baset Hassanien

Copyright copy 2020 Gang Liu and Xiaofeng Li -is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

-e medical data in the intelligent cloud collaborative management system have multidimensional heterogeneous interferenceand there are problems such as low data information update rate and poor push results in the push process -erefore a methodfor multidimensional heterogeneous medical data push was proposed First of all the logical architecture of the multidimensionalheterogeneous data push systemwas determined and the data push function was designed secondly redundant data removal andnoise reduction preprocessing were conducted against the push data correlation rules were used to integrate multidimensionalheterogeneous medical data the weight of medical data was calculated the heterogeneous data matrix was constructed and theintegrated medical data were weighted to eliminate multidimensional heterogeneous interference -e results show that the dataupdate rate of the proposed method is faster the user retention and communication rate are high the data push precision rate isover 80 and the recall rate is as high as 76 -erefore its performance is significantly better than traditional methods

1 Introduction

Multidimensional medical data refer to medical data with amultidimensional structure defined by dimensions andmeasures which is the main object of data online analysis Inrecent years with the development of hospital informati-zation construction computer information technology anddatabase management technology in particular have beenwidely used in the medical field and a large number ofmedical data with different dimensions and forms haveemerged [1] As a result the single data managementplatform can no longer satisfy the medical data processingrequirements [2] In the information age cloud computingtechnology shows obvious advantages and rapid develop-ment To this end an intelligent cloud collaborative man-agement system with collaborative processing function hasbeen developed for the purpose of coordinating and man-aging multidimensional heterogeneous medical data andintegrating medical information and effectively enhancingthe application effectiveness of medical data [3 4] -e main

principle of the design and implementation of an intelligentcloud collaborative management system is to combine thetask collaboration system and information managementsystem through artificial intelligence technology under thepremise of no mutual influence -erefore the intelligentcloud collaborative management system can perform workplanning tasks and information management tasks re-spectively For hospital management high-quality datamakes management efficiency more effective and moreprecise [5 6] In the management decision-making onmedical data it is usually impossible to quickly extract keydata from hospital big data and drive smart healthcare withdata but only through statistical reports submitted by theStatistics Division and Information Division as well asstatistical reports in various discrete systems for manage-ment decisions [7 8] in this case the intelligent cloudcollaborative management system can be analyzed bymultidimensional medical data push

-e mass medical information feature push methodbased on data feature matrix obtained the medical data

HindawiComplexityVolume 2020 Article ID 7574609 14 pageshttpsdoiorg10115520207574609

feature matrix array by using the medical big data featureintelligent collection method [9 10] matched all patientgroup information and part of the patient information byusing the data feature matrix array integrated the corre-sponding patients into the patient group with the highestsimilarity and matched the keywords in the similar patientgroup and the basic key data features pushed in the medicaldata feature matrix array so that the patient group where thepatients were located would push medical messagesaccording to the priority push method of location-basedservice (LBS) [11] Some scholars analyzed the feasibility ofthe application of information push technology in themanagement of tuberculosis patients in the floating pop-ulation and provided a reference for changing the man-agement model -ey used self-made questionnaires toinvestigate the basic conditions of patients treatmentcompliance personal communication tools and other in-formation -e conclusion shows that the information pushtechnology is feasible to manage tuberculosis patients in thefloating population and patient acceptance is high [12]-erefore the application of information push technologyhas good feasibility in the management of migrating tu-berculosis patients and high patient acceptance In theproposed method the Internet-based inpatient health ed-ucation cloud platform was used to push and set up sharingthat is based on the characteristics of hospitals and de-partments [13 14] this method proposed specific nursingunits regularly pushed propaganda and education infor-mation and required its sharing rate to be greater than 80regularly shared the feedback information and conclusionsand analyzed the application effect of health propaganda andeducation cloud platform for Internet-based inpatientsaccording to the patient satisfaction questionnaire thereading amount of propaganda and education content andthe opinions of the patients visited [15]

-e above-mentioned traditional management systemscan achieve better management functions and they are widelyused in actual work and life However according to the studieson their long-term applications the traditional systems haveproblems such as long query time and inability to updatesystem information data in time -erefore it is necessary tointroduce data push technology to improve the updatedfunction of the system In this system optimization multi-dimensional heterogeneous medical data push technology isapplied Among them ldquomultidimensionalrdquo mainly refers tothe dimension of data to be pushed that is the number ofindependent parameters in mathematics ldquoheterogeneousrdquo is aparameter that contains different components and propertiesMultidimensional heterogeneous data is the push data mes-sage that is not unique in dimension and has a differentnetwork structure -rough the application of multidimen-sional heterogeneous medical data push technology thefunction of data push can be realized thereby solving theproblems of long query time and system information datacannot be updated in time in the traditional system andimproving the query ability and update speed of the system

-e main contributions of this study are as follows(1) -e multidimensional heterogeneous medical data

push technology is applied to intelligent cloud collaborative

management system for analysis (2) the logical architectureof the multidimensional heterogeneous data push system isdetermined which lays the foundation for subsequent re-search (3) weighted analysis is made on multidimensionalheterogeneous medical data to eliminate multidimensionalheterogeneous interference which provides a basis forimproving the efficiency of medical data push (4) the pushchannel is selected which greatly improving the effect ofdata push

2 Related Work

By deeply analyzing the BrowserServer (BS) architectureBo [16] designed the corresponding function modulesaccording to the system design principles and equipped witha reasonable database to ensure the quick connection ofdatabase the test results show that the designed electronicmedical record management system could quickly enter andquery medical record data achieve functions such as reliablestorage of medical record data and have an importantauxiliary role for the hospital to grasp a medical record intime Jin et al [17] proposed a wireless intelligent collab-oration system based on generalized spatial modulation-media basedmodulation (GSM-MBM) which could activatemultiple antennas at the relay and install radio frequencymirrors near the antennas different channel paths wereconstructed by activating different radio frequency mirrorsso as to carry extra information bits the system transmissionefficiency average paired error probability and energyconsumption gain were deducted according to relevanttheories and Monte Carlo simulations were conducted as aresult the transmission efficiency was improved and the biterror rate and energy consumption were reduced howeverthe bit error rate was slightly higher under the sametransmission efficiency yet the required the number ofrequired transmitting antennas was greatly reduced therebyreducing the complexity and cost of system implementationAiming at the data characteristics of space structure healthmonitoring Zhang et al [18] put forward the overallframework of the space structure health monitoring Internetof -ings (IoT) system and established the application layerdata processing algorithm by taking the advantages of cloudcomputing in processing intensive tasks completed thedesign of the cloud data management system for spatialstructure monitoring and conducted real-time processingand interactive display over the monitoring information ofmultiple large-span spatial structures including NationalStadium and Hangzhou Railway Station Qin et al [19]designed a medical imaging remote diagnosis cloud serviceplatform to realize automatic uploading centralized storageand management of image data of primary medical insti-tutions as well as the sharing of image information anddiagnosis reports between hospitals the system constructionand research on the image cloud platform were carried outfrom the perspectives of registration of image data thedesign of data storage center and access to image Liu et al[20] proposed a fine-grained access control (FGUR) solutionthat supported user revocation which by introducing theattribute hierarchy into the Comparison-Based Encryption

2 Complexity

(CBE) and combined with the Broadcast Ciphertext-PolicyAttribute-Based Encryption (BCP-ABE) efficiently imple-mented fine-grained access control and real-time userrevocation in the personal health record (PHR) cloudmanagement system compared with CBE the FGUR so-lution shows better performance in encryption overhead anddynamic access permissions

3 Design of a Collaborative ManagementSystem for Multidimensional HeterogeneousMedical Data Push

-e multidimensional heterogeneous medical data pushtechnology is the core technology applied in the intelligentcloud collaborative management system -erefore theimplementation environment and function execution pro-gram of multidimensional heterogeneous medical data pushtechnology is introduced for system design

31 Logical Architecture of the System In the application ofmultidimensional heterogeneous medical data push tech-nology to the intelligent cloud collaborative managementsystem [21 22] the server actively sends messages to thereceiver and the system user does not need to actively checkand update the system can push all multidimensionalheterogeneous medical data to users via the intelligent cloudserver system the system users can receive the most recentmedical data information [23] -erefore the logical ar-chitecture of the Intelligent Cloud collaboration manage-ment system is shown in Figure 1

According to Figure 1 the logical architecture of theintelligent cloud collaborative management system is mainlycomposed of a cloud data layer data management layerapplication interface layer and access layer Among themthe cloud data layer is to integrate the multidimensionalmedical data into a data set after receiving the user-levelaccess information -e data management layer is to processthe integrated data set to realize the collaborative work ofdata and push [24 25] the application interface layer andaccess layer are mainly aimed at the receiver the system canpush the required diversified and heterogeneous medicaldata according to the setting requirements of the user ter-minal so as to realize the collaborative work of data andpush [26]

32DesignofDataPushFunction In the functional design ofthe collaborative management system software using mul-tidimensional heterogeneous medical data push thismethod allows the system to realize the function of medicaldata push based on the traditional collaborative manage-ment function and tries not to interfere or affect the originalcollaborative management function during the operation of

the new function -erefore the push function of multi-dimensional heterogeneous medical data was specificallydesigned in this study It is shown in Figure 2

According to Figure 2 the heterogeneous data infor-mation was mostly collected and the data was transmitted tothe user terminal through the access request the multidi-mensional heterogeneous information was collected into theinformation database and the weights and push decisionswere defined through the access request and then trans-mitted to the user terminal After the users successfullysubscribed to the content of the cloud push platform theplatform needed to send messages to its own users andpush messages to the client in real-time through the longconnection established between the cloud and the client[27 28] Based on traditional push the proposed cloud pushprocess was carried out in the cycle of ldquoSubscription-Col-lection-Decision-Pushrdquo -e cloud push cycle is shown inFigure 3

33 Cross-Layer Preprocessing of Push Data In the use ofheterogeneous sensors to collect and store original data inthe database this study selects part of the multidimensionalheterogeneous medical data in the database as the originalinformation push data Before the medical data was pushedcross-layer preprocessing was first performed to reduce theerror rate of the data push and to improve the data pushquality [29 30] -e entire data cross-layer preprocessingprocess is divided into two steps the removal of redundantdata and the noise reduction of data

Assuming that the original data set is n the data featureset is m(f) and f represents the eigenvalue then theprobability relationship between the original data set n andthe data feature set m(f) can be expressed as follows

Dsensor(f) n 1113946R

1m(f)d(f) (1)

where Dsensor(f) represents the eigenvalue probability ofdata set n the solution result of equation (1) is the proba-bility distribution function of the measured value of themedical data push that is the push data between the datalayer frequency [1 R] According to the solution results m

and n can be divided into three situations It is shown inFigure 4

When the solution result is situation 3 in Figure 4 theredundant data in the medical data set n should be removed

-e noise reduction processing was conducted over dataset the frequency-based eigenvalue probability distributionin the multidimensional collaborative processing underthe normal transmission link is shown in the followingequation

Fx(f) Bvφ

f(vφ)dvdφ f(vφ) u

v2

+ φ2+ cos

12π + ω1113874 1113875

1113970

1113888 1113889 (2)

Complexity 3

where Fx(f) is the eigenvalue probability distributionfunction under the normal transmission link and f(vφ) isthe probability function -e parameters v and ϕ respec-tively represent the collaboration scale of the push node andthe probability of maintaining the collaboration state andthe parameter ω is the angular frequency at which theheterogeneous sensor works

Enhancement processing is performed on the effectivesignals in the medical push data and the enhancement resultC is shown in the following equation

C

Fx(f)vφ2ω

2vφ cosω

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

User layer

Multidimensionalheterogeneous data

Cloud data layer

Datamanagement

layerController

Application interfacelayer

Configure on demand

Subscription module

Provided data storage

Provided accessservices

Access layer

Standardinterface

Differentpermissions

Figure 1 Logical architecture of intelligent cloud collaboration management system


User subscription

Access requestMultidimensional

heterogeneous database

Multiple server dataprocessing

Figure 2 Design of data push function

4 Complexity

According to equation (3) the strength of the effectivedata signal can be enhanced while reducing the noise signal[31 32]

Combined with the above steps the cross-layer pre-processing of push data is completed

34 Integration of Multidimensional Heterogeneous MedicalData -e principle of integrated management of

multidimensional heterogeneous medical data is that thecorrelation rules algorithm defines strong correlation ruleparameters as minimum support and minimum confidence[33] Among them the support degree can be specificallydefined according to the following equation

support(A⟶ B) P(AcupB) (4)

Equation (4) is the probability that multidimensionalheterogeneous medical data A and data B appear

Access request Generatemultimensional

heterogeneous data

Usersubscription

Data collection

Multidimensionalheterogeneous

database

Data push

Output

Datapreprocessing Data integration

Channelselection

Decision

Data weighting

Figure 3 Cloud push cycle

n

n m

m

mn

Eigenvalue

Frequency domain

Situation1

Situation2

Situation3

Dsensor(f) = 1

Figure 4 Process of multidimensional heterogeneous data elimination and optimization

Complexity 5

simultaneously If the value of the calculation result is smallthe correlation between A and B will be low Similarly theconfidence in the correlation rule algorithm can beexpressed as follows

confidence(A⟶ B) P(A|B) (5)

Equation (5) is the probability of B appearing when dataA appears If the calculation result is 100 the correlationbetweenA and Bwill be high-e correlation of any two datain the sample data can be obtained by synthesizing the twoparameters In addition the integration results of multidi-mensional heterogeneous medical data are obtained after thecorrelated data is clustered and integrated

35 Weighted Analysis of Integrated Medical Data -eanalysis of multidimensional heterogeneous medical data isto analyze the content of medical information data -eanalysis results can be used as a reference to eliminatemultidimensional heterogeneous interference and help thesystem select an appropriate push method [34] Besides theweighted analysis of data was performed after the integrationof medical data was completed

First of all the weight of the heterogeneous medical datain each dimension was calculated using the followingequation

TF(i) 1113944n

i1tfj(i) times class(j) (6)

where TF(i) represents the weight of medical data tf(i) isthe frequency of phrases appearing in a certain area inmedical data and class(j) is the weight coefficient of re-gional evaluation which can be obtained by the systemcontroller [35] -e selected data were arranged indescending order of weight and the component analysis ofmedical data was made -e component value is defined byXi then the main component value of each multidimen-sional heterogeneous medical data can be expressed asXi(i 1 2 n) and filled into the following equation soas to get the heterogeneous data matrix

S TF(i) X1 X2 X3 Xn1113858 1113859 (7)

where S is heterogeneous data matrix-e transposedmatrixof the matrix S is obtained using the following equation

ST

TF(i)

X1

X2

X3

Xn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(8)

where T represents the transposed symbol Equations (4)and (5) are multiplied and the average value is obtained asavgX -en the overall heterogeneous value of the multi-dimensional heterogeneous medical data can be calculatedusing the following equation

V ST

1113944 Xi minus μ| avgX minus μ1113868111386811138681113868

1113868111386811138681113868T

1113882 1113883 (9)

where N is the total amount of initial integrated medicaldata and μ is the data offset parameter If multidimensionalheterogeneous medical data needs to be pushed simulta-neously the principal component of each data should beconverted to Xij therefore the conversion result of matrix Sis shown in the following equation

S TF(i)

X11 X12 X1j

X21 X22 X2j

Xi1 Xi1 Xij

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(10)

-e calculation result of V is then expressed in the formof matrix -rough the calculation of V and its diagonalmatrix the weighted calculation eigenvalue W of the datacan be solved It is shown in the following equation

W |V minus kE| S V middot Vand11138681113868111386811138681113868111386811138681113868 (11)

where E represents the identity matrix and Vand parameterrepresents the diagonal matrix of V from which the specificvalue of eigenvalue k can be obtained k and V togethermeasure the heterogeneity of different medical data

36 Selection of Data Push Channel After the weightedanalysis of integrated medical data preparations were doneto push information to the system A push channel needed tobe utilized in this process -erefore it was crucial to selectthe data push channel which would directly affect the ac-curacy of push results

-e selection of data push channel should be specificallyconsidered from two aspects the carrying capacity of thepush channel and the length of the push channel First themaximum efficiency of data transmission between a certainuser u and the push server r should be calculated It is shownin the following equation

Uur maxrisinR

cur

Dur1113890 1113891 (12)

where cur represents the number of data transmissions usedby user u and Dur represents the total amount of datatransmission required by user u In addition the matchingvalue MTMur of the maximum task medical data needs to becalculated

MTMur Uurφ (13)

where φ is the matching parameter All the channels thatmeet the requirements of the above equations are taken ascandidate channels and the channels are arranged accordingto the transmission distance of the channels -e specificarrangement is shown in Figure 5

In Figure 5 the signaling channel controls the con-nection of channels and transfers network managementinformation physical channel reconfiguration can be used toachieve cofrequency hard handover and compression -e

6 Complexity

initial transmission channel is responsible for the initialinput of medical data the retransmission channel is re-sponsible for sending commands and the bidirectionalaccess channel provides diversity gain for wireless trans-mission and improves the reliability of data transmission Ifthe channel environment is the same the value is the sameand the channel that meets the restriction conditions and hasthe shortest push transmission distance can be selected as thepush channel of medical data by calculation

-e optimized multidimensional heterogeneous medicaldata will be pushed through the selected push channelBefore the data push it is necessary to perform statusprocessing on the sender and the receiver Afterwards thecorresponding multidimensional heterogeneous medicaldata are pushed During the push process it is necessary tostrictly control the data push quality using the controller Itis shown in the following equation

SS MTMurvφ2ω

middotp dsensor |t noi( 1113857

p e | noi( 1113857

SSprime 2vφ cosω

(14)

where dsensor represents the channel transmission distance e

represents the control parameter and Ss and Ssprime representthe data quality eigenvalue pushed by the sender and re-ceiver respectively When the values of Ss and Ssprime are withinthe interval [0 1] it is determined that the medical dataconforms to the push quality and is passed When the re-ceiver displays the corresponding push message the intel-ligent cloud collaborative management system implementsthe medical data push function

37 Implementation of Multidimensional HeterogeneousMedical Data Push Management -e management ofmultidimensional heterogeneous medical data push could be

implemented after the selection of the above channels andthe push process is described as follows

Input original multidimensional heterogeneous med-ical data set n and channel selectionOutput push results of multidimensional heteroge-neous medical data

-e intelligent cloud collaborative management systemis initialized and multidimensional heterogeneous medicaldata push is performed -e specific steps are described asfollows

(1) Replacement mapping of multisource heterogeneousdata Multisource heterogeneous data is to expandthe main components of homogeneous data In orderto facilitate mathematical calculations a permutationmatrix Pm is introduced to conduct permutationmapping on the samples obtained on the cloudserver and the result is recorded as y

y yα yb( 1113857T

Pmn (15)

-e purpose is to gather the same part of the currentsample as the isomorphic data in front of the vectordenoted by ya and to place the different part behindthe vector denoted by yb

(2) -e sample structure obtained from the samplingsurvey does not match the overall composition andthis structural difference can be eliminated and re-stored by weighting k different objects are randomlyselected from the data set y of permutation mappingas the initial clustering center

(3) -e weight of each attribute in class k is initialized tothe same value that is the weight of any classCkprime(1le kprime le k) in attribute At(1le tlem) is 1m

SD SD SD SD SD SDFDT FDT FDT FDT FDTFDT FDT FDT FDT

Signaling channel

News1-1

News1-1

News1-1

News1-2

News1ndash3

Initial transmission channel

e terminal sends userfeedback

Rechannel

Two-way access channel

Figure 5 Schematic diagram of push server channel arrangement

Complexity 7

(4) -e weighted measure of dissimilarity of any objectni isin y and class Ci is defined as follows

D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)

According to the above equation the dissimilaritybetween the object and the class centers are calcu-lated and the data object is divided into the classrepresented by the cluster center closest to itaccording to the nearest neighbor principle

(5) -e cluster centers are updated Among them thenumerical attribute part is obtained by calculatingthe average value of the objects in the same class andthe subtype attribute part is obtained by calculatingthe fuzzy class center-e fuzzy class center of the subtype attribute part isexpressed as follows

zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)

(6) -e weights of each attribute in the numerical andsubtype data parts of each class in the fuzzy classcenter are calculated so as to update the informationsource

-e data set of the fuzzy class center conforms to thehigh-dimensional distribution -erefore the cloud datasample weights can be calculated using this high-dimen-sional distribution and the calculation expression of theweight J is as follows

J exp12

| y minus zcl( 1113857

T| 1113944 y minus z

cl( 1113857

minus 11113882 1113883 (18)

-e weight J calculated according to the equation (18)can be used to update the information source After theinformation source is updated the equation for pushingheterogeneous data is as follows

push J 1113944N

i1yi

T (19)

In summary the multidimensional heterogeneousmedical data push is completed as shown in Figure 6

4 Experimental Analysis and Results

41 Experimental Data In order to verify the applicationfunction of the multidimensional heterogeneous medicaldata push technology in the intelligent cloud collaborativemanagement system this study sets up an applicationanalysis experiment -e medical data set is selected asfollows

(1) MIMIC Critical Care Database the public data setfrom MIT lab for computational physiology collects

data from more than 60000 ICUs including de-mographic data physiological signs laboratory testsand drug treatment of patients

(2) Kent Ridge Biomedical Datasets database in thebiomedical field

42 Experimental Steps -is experiment is carried out in aMatlab environment -e GPUmodel of Interreg CoreTM i5-4150 is used for cloud collaboration management systemtraining In this experimental analysis 10 million data areselected from the above two data sets -e experimental dataare processed by the cross-layer preprocessing method inSection 33 and effective data are collected and half of thedata in each data set is randomly selected as the training

Begin

Multidimensional heterogeneous data push architecture

Push data cross-layer preprocessing

Multidimensional heterogeneous medical dataintegration

Integrated data weighting

Selected data push channel

No Did the push data meet therequirements

Yes

Information source update

Push results

End

Figure 6 Multidimensional heterogeneous medical data pushmanagement process

8 Complexity

data and the rest of the data is used as the test data set forexperimental analysis-e two datasets of mimic critical caredatabase and Kent ridge biological datasets are used tofurther optimize the training process of the cloud collabo-ration management system -e training cycle is set to 100cycles and the learning rate is set to 0001

43 Evaluation Criteria

(1) Data update rate it refers to the data update ratereceived by the GPS device which is generallyrefreshed in units of 1 timesecond Generally thedata update rate determines how much data an in-strument can store and it also represents the speed ofdata update Generally speaking a faster update raterepresents better performance

(2) Retention rate it refers to the proportion of users whostill retain the pushed message within a certain periodof time (such as 1ndash6 weeks) which can also reflect theimpact of the model on users to a certain extent

(3) Communication rate it refers to the number ofcommunications between the user and the cloudpush platform in a unit time for testing whether theuser is willing to use the platform for data push -ehigher the communication rate the stronger theuserrsquos willingness to use the system and the better thesystem performance

(4) Precision rate of data push

precision TP

TP + FP (20)

where FP is the number of samples that are incor-rectly pushed TP is the number of samples correctlypushed

(5) Recall rate of data push

recall TP

TP + FN (21)

where FN is the number of samples incorrectly pushedIn order to highlight the application value of multidi-

mensional heterogeneous medical data push technology thearchitecture method based on BS in literature [16] theapproach based on GSM-MBM in literature [17] the ap-proach based on cloud computing in literature [18] thecloud remote collaboration service system in literature [19]the personal health record cloud management system inliterature [20] the priority push based on LBS in literature[21] and the Internet-based inpatient health propagandaand education cloud platform in literature [22] were com-pared with the proposed method so as to verify the ap-plication value of the proposed method

44 Results and Discussion

441 Comparison of the Data Update Rate In this study thesame update task was assigned to different systems -e

update task was divided into 7 stages each update datavolume was 1024MB and the data update rate of differentsystems was recorded It is shown in Figure 7

According to Figure 7 different collaborative manage-ment systems all have higher update rates After calculationthe average update rates of data for the architecture methodbased on BS in literature [16] the approach based on GSM-MBM in literature [17] the approach based on cloudcomputing in literature [18] the cloud remote collaborationservice system in literature [19] the personal health recordcloud management system in literature [20] the prioritypush based on LBS in literature [21] and the Internet-basedinpatient health propaganda and education cloud platformin literature [22] are 92075 91145 89654 8856790521 70 and 65 respectively Due to the applicationof the multidimensional heterogeneous medical data pushtechnology the average update data volume of the intelligentcloud collaborative management system is 1021125MB anincrease of 7778MB and the average update rate is 9961an average increase of 7535-erefore it can be concludedthat the application of multidimensional heterogeneousmedical data push technology can effectively increase thedata update rate of the intelligent cloud collaborativemanagement system

442 Comparison of the Retention Rate Actually the re-tention rate reflects a conversion rate that is the processfrom the initial unstable users into active users stable usersand loyal users With the continuous extension of this re-tention rate statistical process the changes of users in dif-ferent periods can be seen -e higher the retention rate thebetter the system performance can meet user needs and thebetter the performance It is shown in Figure 8

According to Figure 8 the average retention rates of thearchitecture method based on BS in literature [16] theapproach based on GSM-MBM in literature [17] the ap-proach based on cloud computing in literature [18] thecloud remote collaboration service system in literature [19]the personal health record cloud management system inliterature [20] the priority push based on LBS in literature[21] and the Internet-based inpatient health propagandaand education cloud platform in literature [22] are 65125 10 125 15 10 and 75 respectively Incontrast the retention rate of the proposed method is ba-sically stable at 20 and its retention rate is significantlyhigher than other methods -erefore the proposed methodhas significant advantages

443 Comparison of the Communication Rate -e changein the communication rate of different methods is shown inFigure 9 -e communication rates of literature [16] liter-ature [17] literature [18] literature [19] literature [20]literature [21] and literature [22] maintain unchanged be-tween 50 and 65 In contrast the communication rate ofthe proposed method can be as high as about 85 andfinally tend to stabilize at about 75 as the time of ex-periment increases-erefore the proposedmethod is much

Complexity 9

better than other methods -is is mainly because theproposed method selects the push channel in data pushwhich increases the accuracy of medical data informationpush and improves the communication rate

444 Comparison of the Precision Rate -e precision ratemainly depends on the specificity of the retrieved informationand whether the proposed retrieval strategy can accuratelyexpress the usersrsquo real intelligence needs It is shown in Figure 10

Update task number1 2 3 4 5 6 7

Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100

e proposed methodLiterature [16] methodLiterature [17] methodLiterature [18] method

Literature [19] method

Literature [21] methodLiterature [22] method


Figure 7 Data update rate change results

Rete

ntio

n ra

te (

)

Data size (ten thousand)

e proposed methodLiterature [16] methodLiterature [17] methodLiterature [18]method




0200 400 600 800 1000

5

15

20

10

Figure 8 Change of retention rate of different methods

10 Complexity

According to Figure 10 the average precision rates ofthe architecture method based on BS in literature [16] theapproach based on GSM-MBM in literature [17] the ap-proach based on cloud computing in literature [18] thecloud remote collaboration service system in literature [19]

the personal health record cloud management system inliterature [20] the priority push based on LBS in literature[21] and the Internet-based inpatient health propagandaand education cloud platform in literature [22] are 4060 60 58 35 30 and 32 respectively In

Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60





Figure 9 Change of communication rate of different methods

120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times





Figure 10 Precision rate result

Complexity 11

contrast the precision rate of the proposed method is morethan 80 -e reason is that the proposed method deter-mines the system logical architecture of multidimensionalheterogeneous medical data push makes a weighted analysisof multidimensional heterogeneous medical data reducesthe dimension of medical data avoids the removal ofmultidimensional heterogeneous interference and improvesthe precision rate

445 Comparison of the Recall Rate -e change in recallrate of different methods is shown in Figure 11 -e recallrates of the architecture method based on BS in literature[16] the approach based on GSM-MBM in literature [17]the approach based on cloud computing in literature [18]the cloud remote collaboration service system in literature[19] the personal health record cloud management systemin literature [20] the priority push based on LBS in literature[21] and the Internet-based inpatient health propagandaand education cloud platform in literature [22] are notuniform and their entire experimental process is signifi-cantly lower than the proposed method In contrast theprecision rate of the proposed method is up to 76 -ereason is that the proposed method designs a multidi-mensional cross-layer data preprocessing method whichenhances the strength of data signals and promotes theimprovement of the recall rate

5 Conclusions

-e multidimensional heterogeneous medical data is fre-quently generated in the medical research field which affectsthe process of medical data processing As a result in-depthresearch and analysis of multidimensional heterogeneousmedical data push is of great benefit to the development ofmedical systems -is paper analyzes the application ofmultidimensional heterogeneous medical data push in theintelligent cloud collaborative management system andgives the system logic architecture and the multidimensionalheterogeneousmedical data were processed the push channelwas selected and the data push was effectively completed-eresults show that the proposed method has superiorperformance and good data push performance which pro-vides a reference basis for the development of the medicalfield

However in actual medical research due to the diversityof disease types it is impossible to accurately judge thediseases of many patients and the accuracy of the results ofdisease-related data push is affected -erefore in furtherstudy we will focus on introducing intelligent systems intodisease diagnosis for analysis providing a data basis for thediagnosis of diverse diseases helping to obtain more ac-curate disease diagnosis results and laying a foundation forfurther research on the data push system and increasing theintelligence and richness of the system

056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)

Figure 11 Recall rate results

12 Complexity

Data Availability

-e data used to support the findings of this study are in-cluded within the article Readers can access the data sup-porting the conclusions of the study from MIMIC CriticalCare Database and Kent Ridge Biomedical Datasets

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is work was supported by the Humanities and SocialSciences Research Planning Fund Project in Ministry ofEducation under grant number 19YJAZH053 the OpeningProject of State Key Laboratory of Digital PublishingTechnology under grant number cndplab-2020-M003 andMinistry of Education Science and Technology DevelopmentCenter Industry-University Research Innovation Fund un-der grant number 2018A01002

References

[1] W Xiao L Lu C Ji X Yu and D Qi ldquoPrediction of waterpositions in the binding sites of proteins based on collectionsof multi-source heterogeneous atomsrdquo Chemical Biology ampDrug Design vol 95 no 2 pp 224ndash232 2020

[2] Y Guo Design and Implementation of a CollaborativeManagement Platform Based on Face Verification BeijingUniversity of Posts and Telecommunications Beijing China2018

[3] P Sharma ldquoPrediction of heart disease using 2-tier SVM datamining algorithmrdquo International Journal of Advanced Re-search in Big DataManagement System vol l no 2 pp 11ndash242017

[4] J-H Bae and H Y Lee ldquoUser health information analysissystem of urine and feces separable smart toiletrdquo InternationalJournal on Human and Smart Device Interaction vol 5 no 2pp 19ndash24 2018

[5] B Kumwenda J A Cleland G J Prescott K Walker andP W Johnston ldquoRelationship between sociodemographicfactors and selection into UK postgraduate medical trainingprogrammes a national cohort studyrdquo BritishMedical JournalOpen vol 8 no 6 Article ID e021329 2018

[6] A Care F A Ramponi M C Campi et al ldquoA new classi-fication algorithm with guaranteed sensitivity and specificityfor medical applicationsrdquo IEEE Control Systems Letters vol 2no 3 pp 393ndash398 2018

[7] J Yu Z Kuang B Zhang W Zhang D Lin and J FanldquoLeveraging content sensitiveness and user trustworthiness torecommend fine-grained privacy settings for social imagesharingrdquo IEEE Transactions on Information Forensics andSecurity vol 13 no 5 pp 1317ndash1332 2018

[8] Y Yuyu X Jing L Yu X Yueshen XWenjian and Y LifengldquoGroup-wise itinerary planning in temporary mobile socialnetworkrdquo IEEE Access vol 7 pp 83682ndash83693 2019

[9] Y Yin L Chen Y Xu and JWan ldquoQoS prediction for servicerecommendation with deep feature learning in edge com-puting environmentrdquo Mobile Networks and Applicationsvol 25 no 1 pp 1ndash11 2019

[10] H Gao Y Xu Y Yin et al ldquoContext-aware QoS predictionwith neural collaborative filtering for internet-of-things

servicesrdquo IEEE Internet of ings Journal vol 7 no 5pp 4532ndash4542 2020

[11] K Jiang ldquoResearch on feature push of massive medical in-formation based on data feature matrixrdquo Mechanical Designand Manufacturing Engineering vol 48 no 3 pp 59ndash632019

[12] J Mei Y Wang J Zhang et al ldquoFeasibility analysis of theapplication of information push technology in tuberculosismanagement of floating populationrdquo International Medicaland Health Guide vol 24 no 3 pp 320ndash323 2018

[13] J Yu J Li Z Yu and Q Huang ldquoMultimodal transformerwith multi-view visual representation for image captioningrdquoIEEE Transactions on Circuits and Systems for Video Tech-nology vol 15 p 1 2019

[14] J Yu M Tan H Zhang D Tao and Y Rui ldquoHierarchicaldeep click feature prediction for fine-grained image recog-nitionrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence 2019 In press

[15] R Wang B Wu and L Yan ldquoApplication of Internet-basedinpatient health education cloud platformrdquo InternationalJournal of Nursing vol 38 no 12 pp 1758ndash1761 2019

[16] L Bo ldquoDesign and research of hospital electronic medicalrecord management system based on BSc architecturerdquoElectronic Design Engineering vol 25 no 5 pp 46ndash49 2017

[17] X Jin L Yang N Jin et al ldquoPerformance analysis of wirelessenergy carrying collaboration system based on GSM-MBMrdquoJournal of Beijing University of Posts and Telecommunicationsvol 41 no 5 pp 141ndash146 2018

[18] S Zhang Y Luo and Y Shen ldquoDesign of Internet of thingssystem for health monitoring of spatial structure based oncloud computingrdquo Spatial Structure vol 23 no 1 pp 3ndash112017

[19] L Qin W Guo R Cai et al ldquoConstruction and practice ofmedical image cloud remote collaboration service systemrdquoBiomedical Engineering Research vol 27 no 1 pp 111ndash1152018

[20] Q Liu X Liu B Hu et al ldquoFine-grained access controlsupporting user revocation in personal health record cloudmanagement systemrdquo Journal of Electronics and Informationvol 39 no 5 pp 1206ndash1212 2017

[21] J B Cole S K Knack E R Karl G B Horton R Satpathyand B E Driver ldquoHuman errors and adverse hemodynamicevents related to ldquopush dose pressorsrdquo in the emergencydepartmentrdquo Journal of Medical Toxicology vol 15 no 4pp 276ndash286 2019

[22] A F Cartwright M Karunaratne J Barr-Walker N E Johnsand U D Upadhyay ldquoIdentifying national availability ofabortion care and distance from major US cities systematiconline searchrdquo Journal of Medical Internet Research vol 20no 5 p e186 2018

[23] B Fred ldquoGetting value from EHR data Analytics push yieldspayoff at medical center healthrdquo Health Data Managementvol 24 no 3 pp 51ndash53 2016

[24] M Gagolewski R Perez-Fernandez and B De Baets ldquoAninherent difficulty in the aggregation of multidimensionaldatardquo IEEE Transactions on Fuzzy Systems vol 28 no 3pp 602ndash606 2020

[25] M E Klijn and J Hubbuch ldquoApplication of empirical phasediagrams for multidimensional data visualization of high-throughput microbatch crystallization experimentsrdquo Journalof Pharmaceutical Sciences vol 107 no 8 pp 2063ndash20692018

[26] S Ali Al T Ahmad Y Najwa et al ldquoData on the relationshipbetween caffeine addiction and stress among Lebanese

Complexity 13

medical students in Lebanonrdquo Data in Brief vol 20 no 5p 104845 2020

[27] Y Jin X Guo Y Li J Xing and H Tian ldquoTowards stabilizingfacial landmark detection and tracking via hierarchical fil-tering a new methodrdquo Journal of the Franklin Institutevol 357 no 5 pp 3019ndash3037 2020

[28] J Li X Zhang Z Wang et al ldquoDual-band eight-antennaarray design for MIMO applications in 5G mobile terminalsrdquoIEEE Access vol 7 no 1 pp 71636ndash71644 2019

[29] K Chen K S Dhindsa and B Bhushan ldquoCollaborative agent-based model for distributed defense against DDoS attacks inISP networksrdquo International Journal of Security and Its Ap-plications vol 11 no 8 pp 1ndash12 2017

[30] H Sun and Q Hu ldquoA novel deep web data mining algorithmbased on multi-agent information system and collaborativecorrelation rulerdquo International Journal of Future GenerationCommunication and Networking vol 9 no 11 pp 81ndash942016

[31] E Zhang J Fiaidhi and S Mohammed ldquoSocial recom-mendation using graph database Neo4j mini blog twittersocial network graph case studyrdquo International Journal ofFuture Generation Communication and Networking vol 10no 2 pp 9ndash20 2017

[32] R Du Z Pei and J Tian ldquoPersonalized trusted servicerecommendation method based on social workrdquo Interna-tional Journal of Security and Its Applications vol 10 no 9pp 29ndash38 2016

[33] H Xi D Guo and H Zhu ldquoApplication of data mining basedon classifier in class label prediction of coal mining datardquoInternational Journal of Security and Its Applications vol 9no 10 pp 425ndash432 2015

[34] M Francia M Golfarelli and S Rizzi ldquoSummarization andvisualization of multi-level and multidimensional itemsetsrdquoInformation Sciences vol 520 pp 63ndash85 2020

[35] W Shao K Huang Z Han et al ldquoIntegrative analysis ofpathological images and multidimensional genomic data forearly-stage cancer prognosisrdquo IEEE Transactions on MedicalImaging vol 39 no 1 pp 99ndash110 2020

14 Complexity

feature matrix array by using the medical big data featureintelligent collection method [9 10] matched all patientgroup information and part of the patient information byusing the data feature matrix array integrated the corre-sponding patients into the patient group with the highestsimilarity and matched the keywords in the similar patientgroup and the basic key data features pushed in the medicaldata feature matrix array so that the patient group where thepatients were located would push medical messagesaccording to the priority push method of location-basedservice (LBS) [11] Some scholars analyzed the feasibility ofthe application of information push technology in themanagement of tuberculosis patients in the floating pop-ulation and provided a reference for changing the man-agement model -ey used self-made questionnaires toinvestigate the basic conditions of patients treatmentcompliance personal communication tools and other in-formation -e conclusion shows that the information pushtechnology is feasible to manage tuberculosis patients in thefloating population and patient acceptance is high [12]-erefore the application of information push technologyhas good feasibility in the management of migrating tu-berculosis patients and high patient acceptance In theproposed method the Internet-based inpatient health ed-ucation cloud platform was used to push and set up sharingthat is based on the characteristics of hospitals and de-partments [13 14] this method proposed specific nursingunits regularly pushed propaganda and education infor-mation and required its sharing rate to be greater than 80regularly shared the feedback information and conclusionsand analyzed the application effect of health propaganda andeducation cloud platform for Internet-based inpatientsaccording to the patient satisfaction questionnaire thereading amount of propaganda and education content andthe opinions of the patients visited [15]

-e above-mentioned traditional management systemscan achieve better management functions and they are widelyused in actual work and life However according to the studieson their long-term applications the traditional systems haveproblems such as long query time and inability to updatesystem information data in time -erefore it is necessary tointroduce data push technology to improve the updatedfunction of the system In this system optimization multi-dimensional heterogeneous medical data push technology isapplied Among them ldquomultidimensionalrdquo mainly refers tothe dimension of data to be pushed that is the number ofindependent parameters in mathematics ldquoheterogeneousrdquo is aparameter that contains different components and propertiesMultidimensional heterogeneous data is the push data mes-sage that is not unique in dimension and has a differentnetwork structure -rough the application of multidimen-sional heterogeneous medical data push technology thefunction of data push can be realized thereby solving theproblems of long query time and system information datacannot be updated in time in the traditional system andimproving the query ability and update speed of the system

-e main contributions of this study are as follows(1) -e multidimensional heterogeneous medical data

push technology is applied to intelligent cloud collaborative

management system for analysis (2) the logical architectureof the multidimensional heterogeneous data push system isdetermined which lays the foundation for subsequent re-search (3) weighted analysis is made on multidimensionalheterogeneous medical data to eliminate multidimensionalheterogeneous interference which provides a basis forimproving the efficiency of medical data push (4) the pushchannel is selected which greatly improving the effect ofdata push

2 Related Work

By deeply analyzing the BrowserServer (BS) architectureBo [16] designed the corresponding function modulesaccording to the system design principles and equipped witha reasonable database to ensure the quick connection ofdatabase the test results show that the designed electronicmedical record management system could quickly enter andquery medical record data achieve functions such as reliablestorage of medical record data and have an importantauxiliary role for the hospital to grasp a medical record intime Jin et al [17] proposed a wireless intelligent collab-oration system based on generalized spatial modulation-media basedmodulation (GSM-MBM) which could activatemultiple antennas at the relay and install radio frequencymirrors near the antennas different channel paths wereconstructed by activating different radio frequency mirrorsso as to carry extra information bits the system transmissionefficiency average paired error probability and energyconsumption gain were deducted according to relevanttheories and Monte Carlo simulations were conducted as aresult the transmission efficiency was improved and the biterror rate and energy consumption were reduced howeverthe bit error rate was slightly higher under the sametransmission efficiency yet the required the number ofrequired transmitting antennas was greatly reduced therebyreducing the complexity and cost of system implementationAiming at the data characteristics of space structure healthmonitoring Zhang et al [18] put forward the overallframework of the space structure health monitoring Internetof -ings (IoT) system and established the application layerdata processing algorithm by taking the advantages of cloudcomputing in processing intensive tasks completed thedesign of the cloud data management system for spatialstructure monitoring and conducted real-time processingand interactive display over the monitoring information ofmultiple large-span spatial structures including NationalStadium and Hangzhou Railway Station Qin et al [19]designed a medical imaging remote diagnosis cloud serviceplatform to realize automatic uploading centralized storageand management of image data of primary medical insti-tutions as well as the sharing of image information anddiagnosis reports between hospitals the system constructionand research on the image cloud platform were carried outfrom the perspectives of registration of image data thedesign of data storage center and access to image Liu et al[20] proposed a fine-grained access control (FGUR) solutionthat supported user revocation which by introducing theattribute hierarchy into the Comparison-Based Encryption

2 Complexity












1m(f)d(f) (1)





Fx(f) Bvφ


v2

+ φ2+ cos

12π + ω1113874 1113875

1113970

1113888 1113889 (2)

Complexity 3



C

Fx(f)vφ2ω

2vφ cosω

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

User layer


Cloud data layer

Datamanagement

layerController


Configure on demand

Subscription module



Access layer

Standardinterface




User subscription





4 Complexity








heterogeneous data

Usersubscription

Data collection


database

Data push

Output


Channelselection

Decision

Data weighting


n

n m

m

mn

Eigenvalue

Frequency domain

Situation1

Situation2

Situation3

Dsensor(f) = 1


Complexity 5






TF(i) 1113944n



S TF(i) X1 X2 X3 Xn1113858 1113859 (7)


ST

TF(i)

X1

X2

X3

Xn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(8)


V ST


1113868111386811138681113868T

1113882 1113883 (9)


S TF(i)

X11 X12 X1j

X21 X22 X2j

Xi1 Xi1 Xij

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(10)






Uur maxrisinR

cur

Dur1113890 1113891 (12)


MTMur Uurφ (13)



6 Complexity



SS MTMurvφ2ω


p e | noi( 1113857

SSprime 2vφ cosω

(14)








y yα yb( 1113857T

Pmn (15)





Signaling channel

News1-1

News1-1

News1-1

News1-2

News1ndash3



Rechannel



Complexity 7


D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)



zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)



J exp12



cl( 1113857

minus 11113882 1113883 (18)


push J 1113944N

i1yi

T (19)








Begin







Yes


Push results

End


8 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity












1m(f)d(f) (1)





Fx(f) Bvφ


v2

+ φ2+ cos

12π + ω1113874 1113875

1113970

1113888 1113889 (2)

Complexity 3



C

Fx(f)vφ2ω

2vφ cosω

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

User layer


Cloud data layer

Datamanagement

layerController


Configure on demand

Subscription module



Access layer

Standardinterface




User subscription





4 Complexity








heterogeneous data

Usersubscription

Data collection


database

Data push

Output


Channelselection

Decision

Data weighting


n

n m

m

mn

Eigenvalue

Frequency domain

Situation1

Situation2

Situation3

Dsensor(f) = 1


Complexity 5






TF(i) 1113944n



S TF(i) X1 X2 X3 Xn1113858 1113859 (7)


ST

TF(i)

X1

X2

X3

Xn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(8)


V ST


1113868111386811138681113868T

1113882 1113883 (9)


S TF(i)

X11 X12 X1j

X21 X22 X2j

Xi1 Xi1 Xij

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(10)






Uur maxrisinR

cur

Dur1113890 1113891 (12)


MTMur Uurφ (13)



6 Complexity



SS MTMurvφ2ω


p e | noi( 1113857

SSprime 2vφ cosω

(14)








y yα yb( 1113857T

Pmn (15)





Signaling channel

News1-1

News1-1

News1-1

News1-2

News1ndash3



Rechannel



Complexity 7


D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)



zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)



J exp12



cl( 1113857

minus 11113882 1113883 (18)


push J 1113944N

i1yi

T (19)








Begin







Yes


Push results

End


8 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity



C

Fx(f)vφ2ω

2vφ cosω

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

User layer


Cloud data layer

Datamanagement

layerController


Configure on demand

Subscription module



Access layer

Standardinterface




User subscription





4 Complexity








heterogeneous data

Usersubscription

Data collection


database

Data push

Output


Channelselection

Decision

Data weighting


n

n m

m

mn

Eigenvalue

Frequency domain

Situation1

Situation2

Situation3

Dsensor(f) = 1


Complexity 5






TF(i) 1113944n



S TF(i) X1 X2 X3 Xn1113858 1113859 (7)


ST

TF(i)

X1

X2

X3

Xn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(8)


V ST


1113868111386811138681113868T

1113882 1113883 (9)


S TF(i)

X11 X12 X1j

X21 X22 X2j

Xi1 Xi1 Xij

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(10)






Uur maxrisinR

cur

Dur1113890 1113891 (12)


MTMur Uurφ (13)



6 Complexity



SS MTMurvφ2ω


p e | noi( 1113857

SSprime 2vφ cosω

(14)








y yα yb( 1113857T

Pmn (15)





Signaling channel

News1-1

News1-1

News1-1

News1-2

News1ndash3



Rechannel



Complexity 7


D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)



zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)



J exp12



cl( 1113857

minus 11113882 1113883 (18)


push J 1113944N

i1yi

T (19)








Begin







Yes


Push results

End


8 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity








heterogeneous data

Usersubscription

Data collection


database

Data push

Output


Channelselection

Decision

Data weighting


n

n m

m

mn

Eigenvalue

Frequency domain

Situation1

Situation2

Situation3

Dsensor(f) = 1


Complexity 5






TF(i) 1113944n



S TF(i) X1 X2 X3 Xn1113858 1113859 (7)


ST

TF(i)

X1

X2

X3

Xn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(8)


V ST


1113868111386811138681113868T

1113882 1113883 (9)


S TF(i)

X11 X12 X1j

X21 X22 X2j

Xi1 Xi1 Xij

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(10)






Uur maxrisinR

cur

Dur1113890 1113891 (12)


MTMur Uurφ (13)



6 Complexity



SS MTMurvφ2ω


p e | noi( 1113857

SSprime 2vφ cosω

(14)








y yα yb( 1113857T

Pmn (15)





Signaling channel

News1-1

News1-1

News1-1

News1-2

News1ndash3



Rechannel



Complexity 7


D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)



zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)



J exp12



cl( 1113857

minus 11113882 1113883 (18)


push J 1113944N

i1yi

T (19)








Begin







Yes


Push results

End


8 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity






TF(i) 1113944n



S TF(i) X1 X2 X3 Xn1113858 1113859 (7)


ST

TF(i)

X1

X2

X3

Xn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(8)


V ST


1113868111386811138681113868T

1113882 1113883 (9)


S TF(i)

X11 X12 X1j

X21 X22 X2j

Xi1 Xi1 Xij

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(10)






Uur maxrisinR

cur

Dur1113890 1113891 (12)


MTMur Uurφ (13)



6 Complexity



SS MTMurvφ2ω


p e | noi( 1113857

SSprime 2vφ cosω

(14)








y yα yb( 1113857T

Pmn (15)





Signaling channel

News1-1

News1-1

News1-1

News1-2

News1ndash3



Rechannel



Complexity 7


D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)



zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)



J exp12



cl( 1113857

minus 11113882 1113883 (18)


push J 1113944N

i1yi

T (19)








Begin







Yes


Push results

End


8 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity



SS MTMurvφ2ω


p e | noi( 1113857

SSprime 2vφ cosω

(14)








y yα yb( 1113857T

Pmn (15)





Signaling channel

News1-1

News1-1

News1-1

News1-2

News1ndash3



Rechannel



Complexity 7


D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)



zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)



J exp12



cl( 1113857

minus 11113882 1113883 (18)


push J 1113944N

i1yi

T (19)








Begin







Yes


Push results

End


8 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity


D ni zi( 1113857 1113944N

i1Ss n

ti minus At1113872 1113873 + 1113944

N

i1Ssprime C

ti minus At1113872 1113873 (16)



zcl z

clp+1 z

clp+2 z

clp+m1113872 1113873 (17)



J exp12



cl( 1113857

minus 11113882 1113883 (18)


push J 1113944N

i1yi

T (19)








Begin







Yes


Push results

End


8 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity







precision TP

TP + FP (20)



recall TP

TP + FN (21)










Complexity 9




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity




Dat

a upd

ate r

ate (

)

0

20

10

30

40

50

60

70

80

90

100






Rete

ntio

n ra

te (

)






0200 400 600 800 1000

5

15

20

10


10 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity



Com

mun

icat

ion

rate

()

Experiment times0

0

15

30

45

60

75

90

10 20 30 40 50 60






120

40

60

80

100

10 20 30 40 50 60

Prec

ision

rate

()

Experiment times






Complexity 11



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity



5 Conclusions



056

58

60

62

64

66

68

70

72

76

74

5 10 20 25 30 35 40 45 5015





Experiment times

Reca

ll ra

te (

)


12 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity

Data Availability




Acknowledgments


References




























Complexity 13











14 Complexity











14 Complexity

multidimensional heterogeneous medical data push in

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