Systems and methods for using deep learning to generate acuity scores for critically ill or injured patients

An apparatus comprising at least one processor, a communications interface configured for communicating via at least one network, and at least one memory storing computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:

• responsive to receiving an indication of initiation of a medical event, initiate a deep learning model for the patient, wherein the deep learning model (a) comprises a modified recurrent neural network (RNN) with gated recurrent units (GRUs), (b) is executed by an assessment computing entity, (c) has been trained using machine learning, and (d) is configured to generate a prediction for the patient comprising at least one of an acuity score or a mortality prediction;
• responsive to identifying a prediction trigger, automatically update the deep learning model for the patient based at least in part on medical data (a) corresponding to the patient and (b) captured during the medical event as part of one or more time series of medical data;
• automatically generate the prediction using the updated deep learning model;
• and
• automatically provide at least a portion of the prediction such that the at least a portion of the prediction may be used to update an electronic health record corresponding to the patient and/or provided to a clinician for review.

A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising executable portions configured, when executed by a processor of an apparatus, to cause the apparatus to:

• responsive to receiving an indication of initiation of a medical event, initiate a deep learning model for the patient, wherein the deep learning model (a) comprises a modified recurrent neural network (RNN) with gated recurrent units (GRUs), (b) is executed by an assessment computing entity, (c) has been trained using machine learning, and (d) is configured to generate a prediction for the patient comprising at least one of an acuity score or a mortality prediction;
• responsive to identifying a prediction trigger, automatically update the deep learning model for the patient based at least in part on medical data (a) corresponding to the patient and (b) captured during the medical event as part of one or more time series of medical data;
• automatically generate the prediction using the updated deep learning model;
• and
• automatically provide at least a portion of the prediction such that the at least a portion of the prediction may be used to update an electronic health record corresponding to the patient and/or provided to a clinician for review.