Abstract: Techniques for improved machine learning are provided. Activity data describing an activity for a financial account of a resident in a residential care facility is received, and a set of attributes corresponding to the activity is extracted from the activity data, comprising determining a first attribute of the set of attributes by processing unstructured text associated with the activity using one or more natural language processing techniques. A validity score is generated by processing the set of attributes using a trained machine learning model, where the validity score indicates a probability that the activity is valid. In response to determining that the validity score is below a defined threshold, one or more interventions are initiated for the resident.

Abstract: Techniques for improved machine learning are provided. Activity data describing an activity for a financial account of a resident in a residential care facility is received, and a set of attributes corresponding to the activity is extracted from the activity data, comprising determining a first attribute of the set of attributes by processing unstructured text associated with the activity using one or more natural language processing techniques. A validity score is generated by processing the set of attributes using a trained machine learning model, where the validity score indicates a probability that the activity is valid. In response to determining that the validity score is below a defined threshold, one or more interventions are initiated for the resident.

Abstract: Techniques for improved machine learning are provided. Patient data for a patient is received, the patient data relating to an action performed by a caregiver. A medical code is generated for the action by processing the patient data using a machine learning model, and the action is associated with the medical code. An event record, including the medical code, is generated for the patient.

Abstract: Techniques for improved machine learning are provided. Motion data collected during a first time by one or more wearable sensors of a user is received, and a patient associated with the motion data is identified. An action performed by the user is identified by processing the motion data using a machine learning model, and an event record indicating the action, the patient, and the user is generated.

Abstract: Apparatuses and associated techniques for fall detection and prevention via wearable devices are provided. In one aspect, a wearable device is provided including one or more pressure sensors arranged on a bottom portion of the wearable device to collect pressure data from a bottom of a foot of a user, a wireless transmitter communicatively coupled with the one or more pressure sensors, and a power source coupled with the wireless transmitter.