The presentation covers the use of Scalable Predictive Analysis in Critically Ill Patients using a Visual Open Data Analysis Platform (RapidMiner). With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high- complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. The presentation addresses the problem by focusing on an open, visual environment, suited to be applied by the medical community (RapidMiner). As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II / III database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes can be developed. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research. Presentation at Laboratory for Computational Physiology (LCP) Massachusetts Institute of Technology (MIT), Building E25 room 101; December 8th 12-noon Sven Van Poucke, MD, Anesthesiologist, Emergency Physician Department of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium

1. RapidMiner
An entrance to explore MIMIC-III ?
Sven Van Poucke, MD
December 8th 2015

2. Disclosure
Relevant financial arrangement or affiliation with RapidMiner that could
be perceived as a real or apparent conflict of interest in the context of
the subject of this presentation:
Academia License: RapidMiner Studio, Server, Radoop
“RapidMiner Academia provides free or substantially discounted use of
the commercial version of our platform to students, professors,
researchers and other academics at educational institutions.”
No reimbursement for consultancy and/or travel expenses.

5. François Englert
Theoretical physicist 2013 Nobel prize
laureate (shared with Peter Higgs)
Christain de Duve
Nobel prize 1974
(shared with Albert Claude and
George E. Palade)
Corneille
Heymans
1938
1921
Paul Janssen
1926–2003

6. Ziekenhuis Oost-Limburg
Genk Belgium
6

7. Big data gap between actual and
potential data usage ?
• healthy living, wellness, sport
• health care as industrial process
• public health
• MEDICINE
data scientist
budget
domain expert

10. “Evidence 1.0”
• > Physicians as non coding scientist.
• The struggle to apply new medical knowledge….
• Most evidence regarding the effectiveness of medical
innovations has been generated by studies involving
patients who differ from my patients!

11. Cross Industry Standard Process
for Data Mining, CRISP-DM
Shearer C., The CRISP-DM model: the new blueprint for data mining, J Data Warehousing 2000; 5:13—22

12. “Evidence 2.0”

13. Patient characteristics,
descriptive statistics…
Correlation, causation?
Feature selection….
Modeling, validation,
ensemble methods.
Helps model future
(vaccination)
How different actions will
affect therapeutic
performance and point them
toward the optimal choice.

14. https://data-analytics.ghost.io/
this-is-data-science/

15. Querying MIMIC-III(II)

16. Design perspective main screen

17. “Evidence 2.0”

18. Querying MIMIC-III
• 1. Retrieve tables from Repository

19. Querying MIMIC-III
• 1. Retrieve tables from Repository

20. Querying MIMIC-III
• 2. Read database (SQL)

21. Querying MIMIC-III
• 2. Read database (SQL)

22. Querying MIMIC-III
• 2. Read database (SQL)

23. Querying MIMIC-III
• 2. Read database (SQL)

24. Querying MIMIC-III
• 3. Access by Radoop-Hadoop ecosystem

25. Radoop-Hadoop

26. Radoop-Hadoop

27. Descriptive analytics
• Sample
• Filter
• Selection
• Joins
• Generate attributes
• Charts
• ….

28. Descriptive analytics: sample

29. Descriptive analytics: sample

30. Descriptive analytics: sample

31. Descriptive analytics

32. Descriptive analytics

33. Descriptive analytics

34. Descriptive analytics

35. Descriptive analytics

36. Descriptive analytics:
generate attribute

37. Descriptive analytics

38. Descriptive analytics

39. Descriptive analytics: filter

40. Descriptive analytics: filter

41. “Evidence 2.0”

42. Diagnostic analytics

43. Diagnostic analytics

44. Diagnostic analytics

45. Predictive analytics

46. Diagnostic analytics

47. “Evidence 2.0”

48. Predictive analytics

49. Predictive analytics
• Ensemble learning methods in RapidMiner
(Decision Stump, AdaBoost, Random Forest, Bagging,
W-J48, Decision Tree, Naive Bayes, Stacking, Logistic
Regression, Support Vector Machine).

50. Predictive analytics
• AUPRC curves for the 3 best models.
Random Forest (RF) in association with Backward Selection
(BS) and 69 features (left), with Forward Selection (FS) and 8
features (middle) and Gini Selection (GS) and 5 features.

51. Predictive analytics

52. Prescriptive analytics
• Clinical decision support
• Clinical decision automation

53. What is next?
• Text mining:
• Image mining
• Time series
• Web mining

54. Web Mining: Twitter

55. Web Mining: Twitter

56. RapidMiner Streams

57. RapidMiner Server
• The Collaboration tier of the RapidMiner Server for a
team of data scientists and analysts.
• It provides a server-based repository for sharing data
sources, analytical processes, predictive applications,
and best practices.
• Work together more efficiently while accessing,
reusing, and sharing content in a version-controlled,
secure, and centrally managed environment.

58. RapidMiner Server
• The RapidMiner Server Computation tier is optimized
for performance and lets you run big jobs on
enterprise hardware anywhere. With a few clicks you
can push jobs to the server and continuously get
progress feedback, while freeing up your personal
system for other important work.

59. Future projects: RM server
• RapidMiner Server Deployment tier. Easily integrate
with cloud, business, and IT systems through an open
API, a rich set of connectors, and a unique ability to
deploy processes as web services. Set up scheduled
processing, continuously score data in real-time, and
graphically build interactive predictive and visual web
applications to maximize the value of predictive
analytics in your business.

60. RapidMiner Dashboard

61. Sven Van Poucke, MD
Department of Anesthesiology, Critical Care, Emergency Medicine, Pain Therapy
Ziekenhuis Oost-Limburg
Genk, Belgium
RapidMiner
An entrance to explore MIMIC-III ?