1. Apoio à decisão em medicina intensiva usando ECBD Pedro Gago – I P Leiria

2. 2 Intensive care About 250 variables are needed to describe an ICU patient Humans are unable to cope with more than seven variables at a time

3. 3 Objectives Assist ICU doctors by providing accurate and timely predictions for: –the final outcome –organ dysfunction or failure Must overcome natural physician resistance

4. 4 Intensive Care Medicine Condition is severe to the point where it is very difficult for doctors to assess the patient’s state Objective is to stabilize in order to allow transfer to other units Highly invasive and very costly

5. 5 Intensive Care Medicine Data from bed-side monitors may contain useful information Presently such data is not stored

6. 6 Practical Issues Some variables values must be collected manually –Urine output Data QualityData –Errors caused by human intervention –Sensor malfunctions

7. 7 Scores in use SAPS – indicative of the patient’s condition severitySAPS The worst values the first 24 hours of stay in the ICU are collected and the score is calculated

8. 8 Scores in use (2) SOFA – measures organ dysfuntion/failure (worst daily values)SOFA –Cardiovascular, hepatic, central nervous system, respiratory, renal, coagulation Worst daily values Indicative of patient’s condition evolution

9. 9 INTCare Decision Support System to assist ICU doctors Uses available data in order to predict outcome and organ dysfunction/failure Not intended to replace doctors

10. 10 INTCare (2) Semi-autonomous – updates its models as new data arrives Performance expected to improve with time Better results through the use of real time data

11. 11 Architecture

12. 12 Architecture (2)

13. 13 EURICOS II Data from 42 UCI from 9 countries 10 months (1998 and 1999)

14. 14 EURICOS II (2) Data available includes: –case mix (age, origin, etc) –SAPS score –daily SOFA scores –intermediate outcomes –final outcome

15. 15 INTERMEDIATE OUTCOMES Critical Event Suggested Range Continuously out-of-range Intermitently out-of-range Event anytime BP(mmHg)90 – 180≥ 60 mins ≥ 60 in 120 mins < 60 SaO2(%)≥ 90≥ 60 mins ≥ 60 in 120 mins < 80 HR(bpm)60 – 120≥ 60 mins ≥ 60 in 120 mins 180 Diur(ml/hour)≥ 30≥ 2 hours

16. 16 Ensemble Training –Each model is trained on different subsets of the dataset –Each variable has a 70% chance of being selected –Starts with equal weights

17. 17 Ensemble Evolution –Results from batches of records –Weight adjustments according to individual model performance –Worst performing models are deleted from the ensemble –New models are trained using the most recent data and included in the ensemble

18. 18 Ensemble Preliminary results (evolution doesn’t include new models) –Ensemble trained with all cases still outperforms ensemble trained with less cases followed by weight adjustment –Both outperform best individual model

19. 19 Future Work Greater volume of data – deployment in other ICUs Reduce prediction window (next 6 hours instead of next day) Suggest course of action (must be delayed until physician resistance is lowered)

20. 20 INTCare Thank you. Questions? pgago@estg.ipleiria.pt