mining the changes of medical behaviors for clinical pathways
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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
INRTODUCTION
h"p://www.ppthi-‐hoo.com
Changes detection for CP 3
Medical behavior changes in CP 1
Process mining methods for CP 2
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.
INRTODUCTION
h"p://www.ppthi-‐hoo.com
Changes detection for CP 3
Medical behavior changes in CP 1
Process mining methods for CP 2
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.
INRTODUCTION
h"p://www.ppthi-‐hoo.com
Changes detection for CP 3
Medical behavior changes in CP 1
Process mining methods for CP 2
INRTODUCTION
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
h"p://www.ppthi-‐hoo.com
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
Pattern mining Change pattern detection
similarity in time domains
others
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{ }{ }A.φaA.φaaA.φA.φ
A.φaA.φaa
A.φA.φ
A.φA.φ)A.φ,A.φ(sim
baba
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∈∈—
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=)T.φ,T.φ(sim ba
Preprocessing
Pattern mining Change pattern detection
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
Pattern mining Change pattern detection
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
Pattern mining Change pattern detection
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
Pattern mining Change pattern detection
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
Pattern mining Change pattern detection
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
Pattern mining Change pattern detection
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
h"p://www.ppthi-‐hoo.com
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
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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
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