Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: The present disclosure pertains to a system for providing model-based treatment recommendation via individual-specific machine learning models. In some embodiments, the system (i) obtains an audio recording of an individual, (ii) determines, from the audio recording, one or more utterance-related features of the individual; (iii) performs one or more queries based on the one or more utterance-related features to obtain health information (e.g., utterance-related conditions and treatments provided for the utterance-related conditions) associated with similar individuals having similar utterance-related conditions as the subject; (iv) provides the health information associated with the similar individuals to a machine learning model to train the machine learning model; and (v) provides, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.

Abstract: The present disclosure pertains to a system for providing model-based treatment recommendation via individual-specific machine learning models. In some embodiments, the system (i) obtains an audio recording of an individual, (ii) determines, from the audio recording, one or more utterance-related features of the individual; (iii) performs one or more queries based on the one or more utterance-related features to obtain health information (e.g., utterance-related conditions and treatments provided for the utterance-related conditions) associated with similar individuals having similar utterance-related conditions as the subject; (iv) provides the health information associated with the similar individuals to a machine learning model to train the machine learning model; and (v) provides, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.