Abstract: Techniques for improved machine learning are provided. Resident data describing a resident is accessed, and a residential service plan is generated for the resident, comprising extracting a set of features from the resident data and generating a set of predicted fitness scores for a set of services by processing the set of features using a machine learning model trained based on one or more collaborative filtering techniques. The residential service plan is implemented for the resident based at least in part on the set of predicted fitness scores.

Abstract: Techniques for improved computer modeling are provided. Resident data describing a resident of a healthcare facility is received, and a set of resident attributes corresponding to a defined set of features is extracted from the resident data. An acuity score is generated for the resident by processing the set of resident attributes using an acuity model, the acuity model specifying a respective weight for each respective feature in the defined set of features. One or more interventions are initiated for the resident based on the acuity score.

Abstract: Systems, methods and techniques for training and applying machine learning models to predict whether or not one or more individuals will suffer a fall event. In certain embodiments, a machine learning model can include both a static component and a dynamic component, where each component is associated with different types of medical data. In certain embodiments, an adjustment factor based on fall history of individuals is applied to the output of the machine learning model to generate a final score predictive of a fall event. In certain embodiments, the machine learning model is both trained and applied to medical data associated with predetermined forms, and where the predetermined forms include a value range associated with a medical condition.

Abstract: Embodiments herein describe predicting a future event using a sequence of entries. In one embodiment, the sequence of entries are first processed by a static model that includes a dictionary for translating each entry in the sequence to a weight. In one embodiment, these weights can then be combined to provide an input to a machine learning (ML) model. The model then predicts whether the likelihood a future event will occur.

Abstract: A system and method to determine a retention prediction for a caregiver is disclosed. The system includes a database of caregiver data and patient data. The set of caregiver data and patient data are normalized to create a modified set of caregiver and patient data. The modified set of caregiver and patient data defines a set of parameters or inputs from the set of caregiver and patient data and a corresponding employment status. An analysis is performed of parameters correlated with an employment status for each of the caregivers. Based on the correlation and the modified set of caregiver and patient data, a training set of caregiver data is generated that includes at least one parameter that correlates with employment status. The machine learning model is trained using the training set. The training allows a prediction of an employment status associated with the parameter. The accuracy of the trained machine learning model is evaluated.