Abstract: A computerized method includes obtaining a set of entities. The method also includes, for each entity, obtaining data specific to the entity, generating a feature vector based on the data, and processing the feature vector to generate an entity fall likelihood that indicates a likelihood that the entity will experience a fall based on the feature vector. The method further includes determining a subset of entities having entity fall likelihoods that satisfy a threshold. For each entity in the subset, the method includes determining impact scores for parameters of the feature vector associated with the entity and generating a feature list based on the determined impact scores. Each impact score is indicative of an effect of the parameter on the entity fall likelihood for the entity. The feature list is specific to the entity and includes a parameter having the highest impact score.

Abstract: A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training a machine learning model to generate an entity expiration likelihood output, obtaining a set of multiple database entities, and processing, by the machine learning model, feature vector inputs associated with each database entry to generate an entity expiration likelihood output. The instructions include determining a subset of the database entities having the highest entity expiration likelihood outputs, and, for each database entity in the subset, determining output impact scores for parameters of the feature vector input associated with the database entity, generating a feature list based on the determined output impact scores, and automatically selecting an executable sequence according to the entity expiration likelihood output associated with the database entity.