De kritische zorgsector genereert 24/7 astronomische hoeveelheden data, die onze zoektocht naar informatie, kennis, en 'wijsheid' kunnen compliceren. De opeenhoping van klinische data overtreft dikwijls de capaciteit voor effectieve aggregatie en analyse. Bijgevolg wordt het doel de klinische kwaliteit, patiëntveiligheid en geïntegreerde patiëntenzorg onvolledig bereikt. Sven Van Poucke geeft een presentatie over de inzet van een Big Data Research Platform ter ondersteuning van de integratie van een grote klinische database ( MIMIC-II ) in een computer cluster (Hadoop / Hive) met RapidMiner (Radoop) data mining –technologie. Het doel van dit project is om de ontwikkeling van de klinische besluitvorming en closed loop systemen te vereenvoudigen. Bovendien biedt dit een toegankelijk en aantrekkelijk platform voor klinisch onderzoek

1. Data Mining and Clinical Decision Support in
Critical Care Units:
Bridging the 2 Solitudes?
Sven Van Poucke, MD
Dept. Anesthesiology, Intensive Care, Emergency Medicine, Pain Therapy, ZOL, Genk, Belgium
Zhongheng Zhang, MD
Dept. of Critical Care Medicine, Jinhua Municipal Central Hospital, Jinhua Hospital of Zhejiang University, Zhejiang, P.R. China.

2. 2

3. 3

4. Bridging the 2 Solitudes?
“crazy”
“nerd”
“sexy”
“normal”

6. ICU environment

7. 2015, big data in healthcare:
for whom the bell tolls?
 data stream 24/7 ->information, knowledge, wisdom?
 + extra information:
 optimization of treatments
 reduce adverse events
 reduce admission rates
 identification of populations
 - in its infancy: -> Google baseline study

9. 2015, big data in healthcare:
for whom the bell tolls
 ICU:
 10% inpatients beds
 >10% mortality
 >400% cost
 Preprocessed data:
 highly heterogenous, multi-source, multi-scale
 big data (volume, veracity,variety, velocity)
 time-stamped, variable granularity and tenability
 context sensitive feature extraction by fusion of
domain knowledge and data driven methods

10. MIMIC-II

11. Methods
DUA: NIH training course “Protecting Human Research Participants”
Z.Z. and S.V.P (certification number: 1132877 and 1712927)
Data extraction:
Querybuilder interfase database
by SQL with pgADmin PostgreSQL tools (version 1.12.3)
Virtual Machine
Radoop/Hive
38 tables: LABEVENTS, D_PATIENTS, COMORBIDITY_SCORES,
ICUSTAY_DETAIL.

12. Querybuilder
https://mimic2app.csail.mit.edu/querybuilder/

13. VM VirtualBox

14. Data
Patients (n=11944), older than 15 years
Age on ICU admission, sex, Elixhauser comorbidity score,
Type of ICU setting,
Day 1 sequential organ failure assessment (SOFA),
Simplified Acute Physiology Score (SAPS-1),
Time of ICU admission and discharge, date of death,
All measurements of platelet count during ICU stay,
Comorbidities:
hypertension, paralysis, chronic pulmonary disease, diabetes,
renal failure, acquired immunodeficiency syndrome (AIDS), coagulopathy,
obesity and liver disease

15. https://www.youtube.com/watch?v=zToFij3SW8k
RapidMiner-Radoop-Hive

16. Results

17. Results
ICU mortality: 11.5% (n=1378)
Survivors: significantly younger (63.9±18.4 years vs 70.3±16.2 years; p<0.001)
significantly more male patients (57.0% vs 50.7%; p<0.0001)
Mortality was higher for patients admitted on MICU and SICU, not for patients admitted on CCU and CSRU.
SAPS-1 and SOFA scores: significantly higher in the non-survivors (13.9± 4.7 vs 19.3±5.6 and 5.4±3.4 vs 9.6±4.5)
In the non-survivor group, significantly more renal failure, complicated diabetes, coagulopathy and liver disease
The prevalence of normal platelet count on ICU admission was 73.8%.
Low platelet count in 12.5%, 2.3%, 1.2%, 0.6% of the cases, respectively for grade 1, 2, 3 and 4 thrombocytopenia
High platelet count in 9.0%, 0.3%, 0.0%, 0.3% of the cases for low, mild, severe and extreme thrombocythemia
Attribute weights and forward selection by NB classification and SVM selected 10 attributes for mortality prediction
Performance of predictive analysis (NB) resulted in an accuracy of 81.30%±3.58% and an AUC of 0.91±0.02
Performance of predictive analysis(SVM) resulted in an accuracy of 50.71%±0.18% and an AUC of 0.86±0.01

18. Results

19. Results

20. Conclusions
-MIMIC-II is a valuable public database for intensive care research
-We have successfully developed an integration in RapidMiner facilitating the access to MIMIC-II
-This is a major step forward in the analysis of clinical databases using data mining technology
-Essential is the will to communicate between medical professionals and data scientists.
-Is an essential step in the development of clinical decision support and closed loop systems.
-Extensive analysis before change in medical practice and inclusion in clinical guidelines.

22. RapidMiner4Health

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