Abstract: Techniques for operationalizing predicted changes in risk based on interventions are disclosed. In an example method, a computing system stores information about a plurality of patients. The computing system receives, from an ensemble machine learning model, intervention information for a patient including a prediction of risk and change in risk for certain interventions. The computing system generates a prioritized intervention list and provides it to a client device. The computing system receives, from the client device, updated patient engagement data for the patient and adds it to the patient engagement data. The computing system receives, from the ensemble machine learning model that is re-trained using the updated data, updated predictions. The computing system generates an updated prioritized intervention list and provides it to the client device to cause a graphical user interface (“GUI”) to be automatically refreshed with the updated list.

Abstract: Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.

Abstract: Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.

Abstract: Systems and methods for predicting changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may receive, from a first source, first information and receive, from a second source, second information. The computing device may generate patient information by linking the first information and the second information using a linkage, corresponding to a patient. The computing device may generate using one or more trained machine learning models, a risk prediction for the patient and a change in risk prediction for the patient corresponding to an intervention. The computing device may output the risk prediction and the change in risk prediction for the patient.