Abstract: A method, computer system, and a computer program product for integrating uncertainty quantifications into a long-term risk based calendar is provided. The present invention may include receiving data for an organization. The present invention may include generating a long-term risk based calendar using the data received for the organization. The present invention may include presenting the long-term risk based calendar to a plurality of user associated with the organization. The present invention may include providing one or more recommendations to at least one of the plurality of users.

Abstract: Methods, systems, and computer program products for predicting states from patient-caregiver dyadic biomarker data using artificial intelligence are provided herein. A computer-implemented method includes obtaining biomarker data derived from one or more dyads, each dyad comprising at least one patient and at least one caregiver associated with the at least one patient; determining, based on processing the obtained biomarker data, data-based representations of mental distress and/or social rhythm disruption among at least one of the one or more dyads; predicting mental distress and/or social rhythm disruption among a given dyad of at least one patient and at least one caregiver associated with the at least one patient by processing input biomarker data, derived from the given dyad, using artificial intelligence techniques in connection with at least a portion of the data-based representations; and performing automated actions based on the predicting.

Abstract: Described are techniques for A/B testing including a computer-implemented method of identifying, in an A/B testing database, a set of feature values with a statistically significant difference in A/B testing outcomes above a threshold. The method further includes partitioning records in the A/B testing database into a plurality of population strata according to the set of feature values. The method further includes performing A/B testing, and identifying heterogeneous outcomes of the A/B testing for respective strata of the plurality of population strata.

Abstract: A modeling problem can be received. A database of prior calibrated models can be searched to identify a similar problem having features similar to the received modeling problem. The modeling problem can be calibrated using information of the identified similar problem. The accuracy of calibrated modeling problem can be monitored. The modeling problem can be recalibrated until a performance criterion is met. Calibrated modeling problem can be stored in the database.