The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.

1. Taha Kass-Hout, MD, MS Nicolás di Tada October 2008 MACHINE LEARNING AND DISEASE SURVEILLANCE

2. Image source: http://www.birds.cornell.edu/crows/images/deadcrow.jpg Image source: http://farm3.static.flickr.com/2029/2239605500_6ef2fd2295.jpg?v=0

3. LATE DETECTION – RESPONSE DAY CASES Opportunity for control

4. EARLY DETECTION AND RESPONSE DAY CASES Opportunity for control

14. WHY LOCATION MATTERS – CASE MANAGEMENT

16. WHY LOCATION MATTERS – CASE MANAGEMENT

18. WHY LOCATION MATTERS – POP SURVEILLANCE

20. WHY LOCATION MATTERS – POP SURVEILLANCE

22. MODERN DISEASE SURVEILLANCE 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Detection algorithm “ What are we supposed to do with this?” Too many alerts

23. MODERN DISEASE SURVEILLANCE 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Feedback loop

24. ADVANTAGES OF MACHINE LEARNING P(malaria) = 22% P(influenza) = 13% P(other ILI) = 33%

26. HOW TO REPRESENT A DOCUMENT? “ This morning I woke up with fever, I might have a flu.” “ I had a flu last month. […] I had a flu early this week.” flu fever

28. CLASSIFIERS - SVM

30. SVM – NON LINEAR? Φ : x -> φ ( x ) Map to higher-dimension space

31. SVM – FILTERING OR CLASSIFYING Document 1 Document 2 Document 3 Positives Negatives Training Document Training Document Classifier

33. CLUSTERING - AGGLOMERATIVE

34. CLUSTERING - PARTITIONAL

35. BAYESIAN STATISTICS Probability of disease A (flu) once symptoms B (fever) are observed Probability of fever once flu is confirmed Probability of flu (prior or marginal) Probability of fever (prior or marginal)

37. NEURAL NETWORKS - STRUCTURE Hidden Layer Output Layer Input Layer […] […] {I 0 ,I 1 ,……I n } {O 0 ,O 1 ,……O n } Weight

38. NEURAL NETWORK - APPLICATION Event?

41. GENETIC ALGORITHM – MODEL FITNESS Fitness = 1/Area

43. GENETIC ALGORITHM - PROCESS (4, 5 ,6, 3 ,5) (4,3,6,2,5) (5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2) (2,3,4,6,5) (3,4,5,2,6) (3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6) (4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 ) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 )

44. RESULTS – IMPROVED SURVEILLANCE

45. Q&A

47. BACKUP SLIDES