Abstract: In an embodiment, a computer-implemented method comprises receiving at a server computer via a data communication network, a plurality of raw sensor data and a plurality of self-report score values from a plurality of mobile computing devices; using the server computer, for a particular mobile computing device from among the plurality of mobile computing devices; calculating a plurality of inference data based upon transformations of the raw sensor data and the plurality of self-report score values; the server computer accessing via the network a patient electronic health record that is associated with a user of the particular mobile computing device; inputting the raw sensor data and the electronic health record to a plurality of trained machine learning models; operating the plurality of trained machine learning models in an inference phase to generate outputs specifying probabilities that a user of the particular mobile computing device would respond above a particular threshold for each item of the patien

Abstract: In one embodiment, a computer-implemented method can receive, from a mobile computing device, a measurement-based care (MBC) assessment request from a patient. The method can asynchronously assess, using the application server, a plurality of MBC assessment data from the patient, wherein the plurality of MBC assessment data comprises passively collected data values, assessments, programmed heuristics, programmed rules, and model thresholds. The method can asynchronously generate, using a machine learning model and the application server, a plurality of vital sign values for the patient using the plurality of MBC assessment data from the patient. The method can asynchronously determine, using a machine learning model and the application server, stratification group data for the patient using the plurality of MBC assessment data from the patient. The method can send, to the mobile computing device, instructions for presenting a user interface comprising the stratification group data for the patient.