searching and exploring biomedical data vagelis hristidis school of computing and information...

Searching and Exploring Biomedical Data

Vagelis HristidisSchool of Computing and Information SciencesFlorida International University

Vagelis Hristidis, Searching and Exploring Biomedical Data 2

RoadmapWhy is it challenging to search

EMRs?XOntoRank: Leveraging

Ontologies to improve sensitivity in EMR search

ObjectRank: Use authority flow to rank EMR entities

BioNav: Using MeSH to explore the results of PubMed queries


ELECTRONIC MEDICAL RECORDS (EMRs) Adoption of EMRs hard due to political reasons

◦ No unique patient id◦ Confidentiality◦ HIPAA (Health Insurance Portability and

Accountability Act) Move towards XML-based format. One of most promising:

Health Level 7’s Clinical Document Architecture (CDA).

EMRs pose new challenges for Computer Scientists◦ Confidentiality, authentication, secure exchange◦ Storage, Scalability◦ Dictionaries, terms disambiguation◦ Search for interesting patterns (Data Mining)◦ Data Integration, Schema mapping◦ Searching and Exploring


SAMPLE CDA FRAGMENT


CDA Document – Tree View

Vagelis Hristidis, Searching and Exploring Biomedical Data

7

LIMITATIONS OFTraditional IR General XML Search

Text-based search engines do not exploit the XML tags, hierarchical structure of XML

Whole XML document treated as single unit - unacceptable given the possibly large sizes of XML documents

Proximity in XML can also be measured in terms of containment edges

EMRs have known but complex semantics EMRs include free text, numeric data, time

sequences, negative statements. Routine references in EMRs to external

information sources like dictionaries and ontologies.


Syntax vs. Semantics in Schema

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Example – query “Asthma Theophylline”

More details at [Hristidis et al. NSF Symposium on Next Generation of Data Mining ’07]


XOntoRank: Leverage Ontological Knowledge

Algorithm to enhance keyword search using ontological knowledge (e.g., SNOMED) [ICDE’08 poster, ICDE’09 full paper]

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Medical DictionaryM

edic

al D

icti

onar

y

50043002Disorder of

Respiratory system

79688008RespiratoryObstruction

Is a

118946009Disorder of

Thorax

41427001Disorder ofBronchus

Is a

195967001Asthma

Is a

Is a

301229001Bronchial Finding

Is a

405944004AsthmaticBronchitis

Is a

May be

266364000Asthma attack

Is aMay be

955009Bronchial Structure

Finding site of

Finding site of

Finding site of

82094008Lower respiratory tract

structure

Is a


Example 1

q = {“bronchitis”, “albuterol”}

result = Observationcodevalue Bronchitisvalue Albuterol

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Example 2

q = {“asthma”, “albuterol”}

result = ???

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XOntoRankA CDA node may be associated to a

query keyword w through ontology.XOntoRank first assigns scores to

ontological concepts◦ OntoScore OS(): Semantic relevance of a

concept c in the ontology to a query keyword w.

Then, given these scores, assign Node Scores NS() to document nodes

Other aggregation functions are possible.

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Computing OntoScore of Concept Given Query KeywordThree ways to view the ontology

graph:◦As an unlabeled, undirected graph.◦As a taxonomy.◦As a complete set of relationships.

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Authority Flow Ranking in EMRs

A subset of the electronic health record dataset.

Work under submission.

EventsPlan TimeStampCreated=”2004-11-03 11:57:00.0" Events=”….small residual pericardial effusion…..”

Hospitalization TimeStampCreated=”2004-10-27 22:00:00.0" History=”18 year old boy with an aggressive form of chest lymphoma…” Allergies = “NKDA”…...

Cardiac PatientID=”1438" Complication=”apical impulse … Echo-large increasing pericardial effusion…”

Employee TimeStampCreated=”2004-12-23 14:03:00.0" Title=”Pediatric Cardiologist”….

EventsPlan Events=“4 month old baby… pericardial effusion...”

Medication TimeStampCreated=”2003-02-13 21:57:00.0"..

Hospitalization History = “48 year old..”

v1v7

v2v3

v4

v5v6

prescribed_to

recorded_by

recorded_by

Query: “pericardial effusion”


Authority Flow Ranking

Schema of the EMR dataset

Hospitalization

EmployeeAssociated_

Events

Patient Medication

A-E

P-M H-M

M-E

A-H H-E

P-E

created_by

reco

rded

_by

pres

crib

ed_b

y

of prescribed_to

forcreated_by


User Study


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Explaining Subgraph


User Study Results

00.10.20.30.40.50.60.70.80.9

1

CO085BM25 BM25 CO085 CO030

Ave

rag

e S

ensi

tivity

00.10.20.30.40.50.60.70.80.9

1

CO085BM25 BM25 CO085 CO030

Ave

rage

Spe

cific

ity

Mean Sensitivity Mean Specificity

BM25: Traditional Information Retrieval Ranking FunctionCO: Clinical ObjectRank (Authority Flow)


Biological Databases (cont’d) – Results Navigation [ICDE09, TKDE 2010]

With SUNY Buffalo.Demo at

http://db.cse.buffalo.edu/bionav/Most publications in PubMed

annotated with Medical Subject Headings (MeSH) terms.

Present results in MeSH tree.Propose navigation model and

smart expansion techniques that may skip tree levels. 22

BioNav: Exploring PubMed Results

Static Navigation Treefor query “prothymosin”

MESH (313)Amino Acids, Peptides, and Proteins (310)

Proteins (307)Nucleoproteins (40)

Biological Phenomena, … (217)Cell Physiology (161)

Cell Growth Processes (99)

Genetic Processes (193)Gene Expression (92)

Transcription, Genetic (25)

95 more nodes

2 more nodes45 more nodes

4 more nodes

3 more nodes15 more nodes

10 more nodes1 more node

Histones (15)

- Query Keyword: prothymosin

- Number of results: 313

- Navigation Tree stats:

• # of nodes: 3941• depth: 10• total citations: 30897

Big tree with many duplicates!

23Vagelis Hristidis, Searching and Exploring Biomedical Data

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BioNav: Exploring PubMed Results

Reveal to the user a selected set of descendent concepts that:(a) Collectively contain all results(b) Minimize the expected user navigation costNot all children of the root are necessarily revealed as in static navigation.



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BioNav Evaluation

02468

101214161820

Overall Navigation Cost(# of Concepts Revealed + # of EXPAND Actions)

Static BioNav

References Abhijith Kashyap, Vagelis Hristidis, Michalis Petropoulos, and Sotiria Tavoulari.

Effective Navigation of Query Results Based on Concept Hierarchies. IEEE Transactions on Knowledge and Data Engineering (TKDE) 2010

Fernando Farfán, Vagelis Hristidis, Anand Ranganathan, and Michael Weiner. XOntoRank: Ontology-Aware Search of Electronic Medical Records. IEEE International Conference on Data Engineering (ICDE) 2009

Abhijith Kashyap, Vagelis Hristidis, Michalis Petropoulos, and Sotiria Tavoulari. BioNav: Effective Navigation on Query Results of Biomedical Databases. IEEE International Conference on Data Engineering, ICDE 2009

Vagelis Hristidis, Fernando Farfán, Redmond P. Burke, Anthony F. Rossi, Jeffrey A. White. Information Discovery on Electronic Medical Records. National Science Foundation Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM) 2007

Supported by NSF IIS-0811922: Information Discovery on Domain Data Graphs, 2008-

2011 NSF CAREER IIS-0952347, 2010-2015

26Vagelis Hristidis, Searching and Exploring Biomedical Data

http://users.cis.fiu.edu/~vagelis/publications/BioNav%20TKDE2009.pdf

http://users.cis.fiu.edu/~vagelis/publications/XOntoRankICDE09.pdf

http://users.cis.fiu.edu/~vagelis/publications/XOntoRankICDE09.pdf

http://users.cis.fiu.edu/~vagelis/publications/BioNavICDE09.pdf

http://users.cis.fiu.edu/~vagelis/publications/BioNavICDE09.pdf

http://users.cis.fiu.edu/~vagelis/publications/InformationDiscoveryEMR-CameraReady.pdf

searching and exploring biomedical data vagelis hristidis school of computing and information...

Documents

biomedical data slide

numeric data

generation of data mining

emr search objectrank

keyword search

terms disambiguation

xml tags

emr entities bionav