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.
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
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
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)