mining the changes of medical behaviors for clinical pathways

Mining the Changes of Medical Behaviors for Clinical Pathways

Zhengxing Huang, Chenxi Gan, Huilong Duan

INRTODUCTION

h"p://www.ppthi-‐hoo.com

Changes detection for CP 3

Medical behavior changes in CP

1

Process mining methods for CP 2

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Medical behavior changes in CP 1


INRTODUCTION

p  Medical behavior: medical activity at

specific timestamp

p  How changes happen:

time-linked process, i.e.,

medical evolvements on

conditions & traditions

p  What changes indicate:

Needing direction for clinical pathway

improvement, i.e.,

to include what’s new;

to remove what’s unnecessary;　

minute adjustment.

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INRTODUCTION

p  CANs:

analyze from external perspective, e.g.,

LOS, costs, referral rate;

workflow pattern mining;

model segmentation;

variation prediction.

p  CANNOTs:

have insights of medical behavior changes:

discover critical medical behaviors;

discover and analyze medical behavior

changes.

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p  Objective:

For CPs in two different time periods,

to discover medical behavior patterns;

to design similarity measurement indicators;

to detect the significant changes between

patterns in both activities and timestamps.

p  Contributions:

effectively handle the mass mount of complex

CP data;

provide detailed information on medical

p  Objective: behavior changes;

For CPs in two different time periods, provide references for clinical experts

to discover medical behavior patterns; scientifically design and improve CPs.

METHOD

METHOD Billedet kan ikke vises. Computere


Clinical Behavior Record

Clinical Event Log

Medical Behavior Pa6erns Change Pa6erns

Billedet kan ikke vises. Computeren har muligvis ikke hukommelse nok til at åbne billedet, eller billedet er muligvis blevet beskadiget. Genstart computeren, og åbn derefter filen igen. Hvis det røde x stadig vises, skal du muligvis slette billedet og indsætte det igen.

Preprocessing

Medical Behavior

Pa6erns Mining Change Pa6ern Detec9on： ① Category ② Similarity Measurement ③ Support Change Measurement

Preprocessing

Pattern mining Change pattern detection

Clinical Event

Pa9ent Trace

Clinical Event Log

(Frequent) Medical Behavior Pa6ern Support

p  previous work:

Summarizing clinical pathways from event logs

Z Huang , X Lu, , H Duan , W Fan

Journal of Biomedical InformaCcs, 46(1): 111–

127,

2013

p  method basis:

dynamic programming (DP)-‐based log

segmentaCon algorithm;

frequent-‐pa"ern mining methods, such as Apriori

and FP-‐growth

Preprocessing


similarity in time domains

others

tttttttttttt

babababa

baba

,0

,0),(max—),(min,),min(—),(max

),(max—),(min ——

——

——

>++

++

++

{ }{ }A.φaA.φaaA.φA.φ

A.φaA.φaa

A.φA.φ

A.φA.φ)A.φ,A.φ(sim

baba

ba

ba

baba

∈∈—

∈∈

+==

=)T.φ,T.φ(sim ba

Preprocessing


Change Pa6ern Abs. Implica9on

Perished Pa"ern PP pa"erns which have perished

Added Pa"ern AP new pa"erns

Unexpected Change UC pa"erns that have change in some way

Emerging Pa"ern EP pa"erns with high similarity and significant change of support

Preprocessing


Log A

Log B

SUPP

SIM

Pa6ern set A

Pa6ern set B

SUPP

SC>=SCT? SIM>PMTH

Emerging Pattern

Added Pattern

Unexpected Change

Perished Pattern SIM<PMTL

PMTL≤SIM≤PMTH

Y α

EXPERIMENTS & RESULTS


Crucial clinical activities with represented alphabets Crucial clinical activities with represented alphabets

Abstraction Clinical activities a Admission b Color ultrasound examination c ECG d Pulmonary function tests e Cardiac color Doppler ultrasound f Catheterization g Venous catheterization h Indwelling urethral catheterization start i Indwelling urethral catheterization complete j Postoperative drainage start k Postoperative drainage complete l Atomizing inhalation m Pleural puncture n Radical surgery of lung cancer o Bronchoscopic treatment p Pleaural effusion B-ultrasound and positioning q Determination of left ventricular function r Configuration of anti-tumor chemotherapy s Infrared treatment t Cleansing enema u Electrolyte v Liver and kidney of sugar w Routine blood test x High-sensitivity CRP ya Anesthetic (isoflurane) yb Anesthetic (sevoflurane) z Discharge ct CT examination

Preprocessing Preprocessing


Billedet kan ikke vises. Computeren har muligvis ikke hukommelse nok til at åbne billedet, eller billedet er muligvis blevet beskadiget. Genstart computeren, og åbn derefter filen igen. Hvis det røde x stadig vises, skal du muligvis slette billedet og indsætte det igen.

Frequent medical behavior patterns

minsupp=0.3

p  time stages:

Admission (Ad.)

p  time stages: Pre-‐OP Days

Admission (Ad.)

Pre-‐OP Days

OperaCon (OP) Days

Preprocessing


Log No. α = 0 α = .25 α = .5 α =.75 α =1

2008-

2009

(LA)

1 1.000 0.854 0.708 0.562 0.417

2 1.000 0.886 0.773 0.659 0.545

3 0.000 0.019 0.038 0.058 0.077

4 0.933 0.789 0.645 0.501 0.357

5 0.933 0.789 0.645 0.501 0.357

6 0.389 0.349 0.310 0.270 0.231

7 0.389 0.308 0.228 0.150 0.200

8 0.389 0.308 0.228 0.275 0.333

9 0.143 0.157 0.202 0.268 0.333

10 0.143 0.157 0.171 0.186 0.200

11 0.143 0.157 0.171 0.186 0.200

2011

(LB)

1 1.000 0.854 0.708 0.562 0.417

2 1.000 0.886 0.773 0.659 0.545

3 0.933 0.789 0.645 0.501 0.357

4 0.100 0.158 0.217 0.275 0.333

5 0.143 0.157 0.171 0.214 0.286

Similarity values on different values of α

Impact of parameter α on SIMa (A), SIMb (B)

(A)

(B)

Preprocessing


Patterns

Perished pattern a3, a10, a11

Added pattern b5

Unexpected change a4, a5, a6, a7, a8, a9

Emerging pattern None

Others a1 ( 0)φ,φ(SC 1b1a = )

a2 ( 0)φ,φ(SC 1b2a = )

Pattern Period Mode Sim.T Sim.A Changes

(Abs.)

a3 Pre-OP PP 0.000 0.077 -

a10 Dis. PP 0.143 0.200 -

a11 Dis. PP 0.143 0.200 -

b5 Dis. AP 0.143 0.286 -

a4 OP UC 0.933 0.357 ya, u, v, w, x

a5 OP UC 0.933 0.357 ya, u, v, w, x

a6 Post-OP UC 0.389 0.231 u, v, w, x

a7 Post-OP UC 0.389 0.200 u, v, w, x

α=0.5, SCT=0.4, PMTL=0.2, PMTH=0.7

Results of change pattern detection Details for change patterns

totaled 10 medical behavior change patterns

CONCLUSION &

DISCUSSION

CONCLUSION

Summary

p  core methods:

change pa"ern detecCon: similarity measurement & pa6ern divide

p  contributions:

accurate detecCon of 4 types of change pa"erns;

detailed review of the changes’ degree and direcCon;

p  significance:

handle the mass mount of complex CP data;

summarize medical experiences;

help CP analysis and improvement.

Innovation p  Specialized analysis for medical behavior changes in CP

p  First employs emerging pattern detection method to CP analysis

FUTURE WORK

larger data sets & more diseases


NOT only within one institution

•  between different institutions

to analyze impacts of factors on clinical pathway

execution, such as local environment, healthcare

conditions, medical traditions

•  between templates and actual patterns for CP adherence check

REFERENCES


1.  Bromberg PM. Shadow and substance: A relational per-spective on clinical process. Psy-choanalytic Psychology

1993: 10(2): 147-168.

2.  Huang Z, Lu X and Duan H. On mining clinical pathway patterns from medical behaviors. Artificial Intelligence in

Medicine 2012: 35–50.

3.  Dong G and Li J. Efficient mining of emerging patterns: Discovering trends and differ-ences. In Proceedings of the

fifth International Conference on Knowledge Discovery and Data Mining, San Diego, USA, (SIGKDD 99), 1999:

43-52.

4.  Huang Z, Lu X, Duan H and Fan W. Summarizing clinical pathways from event logs. Journal of Biomedical

Informatics 2012, accepted.

5.  Agrawal R and Srikant R. Fast algorithms for mining asso-ciation rules. 1994 International conference on very large

data bases, 1994: 487-499.

6.  Han J, Pei J and Yin Y. Mining frequent patterns without candidate generation: a frequent-pattern tree approach.

Data Min Knowledge Discovery 2004: 8:53-87.

7.  Combi C, Gozzi M, Oliboni B, Juarez JM and Marin R. Temporal similarity measures for querying clinical work-flows.

Artificial Intelligence in Medicine 2009: 37-54.

8.  Peleg M, Mulyar N and Van Der Aalst WMP. Pattern-based analysis of computer-interpretable guidelines: Don't

forget the context. Artificial Intelligence in Medicine 2012: 73-74.

WELCOME FOR QUESTIONS

THANKS !

Biomedical InformaCcs, ZJU

mining the changes of medical behaviors for clinical pathways

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