Abstract: Systems, methods, and media are provided for predicting fall risk for a post-acute care patient. Patient data for the post-acute care patient is received. Features from the patient data are extracted. The features include one or more polypharmacy features. Other features may include lab or vital features. Based on the features extracted from the patient data, a prediction of the post-acute care patient suffering a fall within the future is generated using one or more machine learning models. The one or more machine learning models may be trained using one or more of binary features, continuous features, categorical features, free-text features, or a combination thereof. An action is initiated based on the prediction of the post-acute care patient suffering a fall. The action is associated with reducing the risk of the patient fall, such adjusting the lighting in the patient's room, for example.

Abstract: A decision support tool is provided for discharging a patient by predicting the probability of a patient's readmission based on information prior to discharge. The information used to make the prediction may include labs, vitals, diagnoses, and medications from prior encounters and from the current encounter. At least some of this information may be used to compute a readmission prediction. One or more recurrent neural networks may be utilized to generate the prediction, and one or more sequences may be generated from the patient information to feed into the one or more recurrent neural networks. Based on the prediction, one or more actions may be initiated to reduce the probability of readmission.