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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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Presentation on theme: "Analysis of Patient Treatment Procedures The BPI Challenge Case Study R. P. Jagadeesh Chandra ‘JC’ Bose Wil M.P. van der Aalst."— Presentation transcript:

1 Analysis of Patient Treatment Procedures The BPI Challenge Case Study R. P. Jagadeesh Chandra ‘JC’ Bose Wil M.P. van der Aalst

2 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

3 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

4 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

5 Heuristic Net © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

6 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

7 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

8 Dissecting the event log © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

9 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

10 The Diagnosis Perspective © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

11 The Diagnosis Perspective FIGO Staging Classifications and Clinical Practice Guidelines for Gynaecological Cancers J.L. Benedet, H. Hender, H. Jones 3rd, H.Y. Ngan and S. Pecorelli International Journal of Gynaecology and Obstetrics (2009) 70(2), “Ninety percent of cancers are squamous in origin, while melanomas, adenocarcinomas, basal cell carcinomas, …, and other malignancies also occur” © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

12 The Diagnosis Perspective © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

13 Heterogeneity of cases © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

14 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 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 © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

15 Grouping homogenous cases- The Diagnosis Perspective © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

16 The Treatment Perspective © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

17 Organizational Perspective to Derive Artifacts © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

18 The Time Perspective (1) © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

19 The Time Perspective (2) © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

20 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

21 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

22 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

23 Control-flow Discovery

24 Workflow of Treatment Procedures on Patients Diagnosed for M11 Raw Event Log a.Select cases whose diagnosis code combination is {M11} – 162 cases, 207 event classes, events b.Segregate cases from (a) into urgent and non-urgent cases −137 non-urgent cases, 143 event classes, events −25 urgent cases, 173 event classes, events c.Transform the logs based on the notion of artifacts −136 non-urgent cases, 21 event classes, events −25 urgent cases, 18 event classes, events © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

25 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

26 Non-urgent cases - Pathology © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

27 Non-urgent cases - Radiology © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

28 Non-urgent cases- GLCC © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

29 Non-urgent cases –GLCC, Blood Count Tests © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

30 Urgent Cases © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

31 Urgent Cases - GLCC © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

32 Process Diagnostics

33 Patients Diagnosed with M13 Raw Event Log Select cases whose diagnostic code combination is {M13} cases, 272 event classes, events Select cases who have been administered with treatment code combination {803} – 23 cases, 135 event classes, events Segregate urgent and non-urgent cases −15 non-urgent cases, 110 event classes, events −8 urgent cases, 94 event classes, events © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

34 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

35 Non-urgent cases e7 - SCC using EIA a0: CEA - tumor marker using MEIA ABO blood group and Rh factor (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

36 Urgent Cases © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

37 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

38 Urgent Cases © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

39 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

40 Reflections on the Challenge Open ended challenge a boon as well as a disadvantage Real questions  Real results Objective evaluation (No) © R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst, Eindhoven University of Technology, 2011

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