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Analysis of Patient Treatment Procedures The BPI Challenge Case Study R. P. Jagadeesh Chandra ‘JC’ Bose Wil M.P. van der Aalst
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Analysis of Patient Treatment ProceduresThe BPI Challenge Case Study
R. P. Jagadeesh Chandra ‘JC’ Bose
Wil M.P. van der Aalst
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Challenge
• provides participants with a real-life event log• analyze this data using whatever techniques
available• can focus on a specific aspect of interest and
analyze this aspect in great detail• may report on a broader range of aspects
• each aspect does not have to be developed in full detail• judged on its completeness of analysis
• use any tools, techniques, methods at your disposal• techniques developed or implemented specifically
for this challenge are welcome!!
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Event Log
• taken from a Dutch Academic Hospital• each case is a patient of a Gynaecology department• many attributes have been recorded that are relevant
to the process
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Event Log
• taken from a Dutch Academic Hospital• each case is a patient of a Gynaecology department• many attributes have been recorded that are relevant
to the process
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Heuristic Net
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Overview of our approach
preprocess(filtering/splitting
of event log)
analyze(enhanced fuzzy
mining and trace alignment)
interpret
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Overview of our approach
preprocess(filtering/splitting
of event log)
analyze(enhanced fuzzy
mining and trace alignment)
interpret
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Dissecting the event log
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Diagnosis Perspective
M11, M12, M13, etc.
Plaveiselcelca_ cervix st IIb,Clearcell ca. ovarium st Ia ,Adenoca: corpus uteri st IVa
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Diagnosis Perspective
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Diagnosis Perspective
FIGO Staging Classifications and Clinical Practice Guidelines for Gynaecological CancersJ.L. Benedet, H. Hender, H. Jones 3rd, H.Y. Ngan and S. PecorelliInternational Journal of Gynaecology and Obstetrics (2009) 70(2), 209-262
“Ninety percent of cancers are squamous in origin, while melanomas, adenocarcinomas, basal cell carcinomas, …, and other malignancies also occur”
http://www.figo.org
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Diagnosis Perspective
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Heterogeneity of cases
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
1. R.P.J.C. Bose and W.M.P. van der Aalst, Context Aware Trace Clustering: Towards Improving Process Mining Results, SIAM International Conference on Data Mining (SDM), 2009 pp 401-412.
2. R.P.J.C. Bose and W.M.P. van der Aalst, Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models, BPM Workshops 2009, vol 43 of LNBIP, pp 170-181
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Grouping homogenous cases-The Diagnosis Perspective
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Treatment Perspective
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Organizational Perspective to Derive Artifacts
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Time Perspective (1)
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
The Time Perspective (2)
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Urgent vs. Non-urgent Cases
• haemoglobin photoelectric• haemoglobin photoelectric-urgent• platelet count • platelet count-urgent
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Preprocessing-Summary
• Five perspectives• Diagnosis• Treatment• Organizational• Time• Urgent and non-urgent
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
preprocess(filtering/splitting
of event log)
analyze(enhanced fuzzy
mining and trace alignment)
interpret
Control-flow Discovery
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Workflow of Treatment Procedures on Patients Diagnosed for M11
• Raw Event Loga. Select cases whose diagnosis code combination is
{M11} – 162 cases, 207 event classes, 11.280 events
b. Segregate cases from (a) into urgent and non-urgent cases
− 137 non-urgent cases, 143 event classes, 6.225 events− 25 urgent cases, 173 event classes, 5.055 events
c. Transform the logs based on the notion of artifacts− 136 non-urgent cases, 21 event classes, 1.561 events− 25 urgent cases, 18 event classes, 1.118 events
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Workflow for non-urgent cases
General Lab Clinical Chemistry
Radiology
Pathology
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Non-urgent cases - Pathology
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Non-urgent cases - Radiology
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Non-urgent cases- GLCC
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Non-urgent cases –GLCC, Blood Count Tests
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Urgent Cases
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Urgent Cases - GLCC
Process Diagnostics
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Patients Diagnosed with M13
• Raw Event Log• Select cases whose diagnostic code combination is
{M13} - 252 cases, 272 event classes, 14.611 events• Select cases who have been administered with
treatment code combination {803} – 23 cases, 135 event classes, 3.329 events
• Segregate urgent and non-urgent cases− 15 non-urgent cases, 110 event classes, 1.961 events− 8 urgent cases, 94 event classes, 1.368 events
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Non-urgent cases
Consensus sequence forms the backbone of
the process
Deviations, exceptional behavior, rare event executions are captured in regions that are
sparsely filled or in regions that are well conserved with a few rare gaps
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Non-urgent cases e7 - SCC using EIA
a0: CEA - tumor marker using
MEIA
ABO blood group and Rhfactor (e4) Rh factor using centrifuge method (c3)
e8 - cephalin time-coagulation
test
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Urgent Cases a0: CEA - tumor marker using
MEIA
Lots of activities skipped
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Urgent Cases
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Take Home Points
• Things may look complex/uninteresting unless looked from a right perspective
• Preprocessing is extremely important, but unfortunately often neglected
• Treatment procedures are rather simple and sequential• Cases share a lot in common with very little deviations
Beautiful woman,
wolves, tiger, eagle,
horse..
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
Reflections on the Challenge
• Open ended challenge• a boon as well as a disadvantage
• Real questions Real results• Objective evaluation
(No)(No)
© R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011
