BeatDB is a scalable machine learning framework that allows researchers to build predictive models through physiological waveform mining and analysis. One of it's strengths is it's flexibility, allowing for users to define features to track, conditions to detect, and filters to apply with short user-defined scripts. BeatDB is integrated within Amazon Web Services (AWS), allowing for users to run computations in parallel in the cloud. Because of these features, BeatDB allows researchers and scientists to cut down on time needed for prediction studies and data processing without sacrificing any of the parameterization and specificity to the data possible with custom (and often single-use) scripts.

"Patients Like Me" for Precision Medicine (PM2) In data-driven precision medicine, fast yet accurate prediction of acute and critical events based on physiological time series is of crucial importance especially in intensive care units. In such setting, promptness is demanded, so if a task can be completed dramatically faster, it is often acceptable to tolerate a slight decrease in accuracy. We build a system for scalable patient record retrieval and event prediction based on locality-sensitive hashing (LSH) for high-dimensional, massive physiological time-series data. It has a significantly faster querying time, while maintaining the accuracy in a competitive range in comparison to the linear, exhaustive k-nearest neighbor search. The prediction based on LSH is essentially a two-step process of first quickly retrieving "patients like me", the approximate nearest neighbors of our query of interest by LSH, and second, extrapolating the information of nearest neighbors for prediction.

OSaaS is an integrative machine learning and econometric framework that allows researchers to build prediction and causation models drawing on observational data. OSaaS seeks to aid decision making in contexts where the ground truth is not well known and uncertainty exists around the effectiveness of one's interventions. By leveraging big data in different domains (health, transportation, commercial), OSaaS seeks to make explicit and transparent the modeling choices made by researchers that ultimately inform key decisions (what patient receives what treatment and when, transportation infrastructure and policy decisions, insurance pricing).

This project is supported by Philips.