Abstract: Systems, apparatuses and methods may provide for technology that assigns confidence levels to data bins containing similarity data and length of stay data, wherein the similarity data and the length of stay data correspond to a plurality of previous admissions. Additionally, the confidence levels may be weighted based on a distribution metric that assigns higher weights to denser regions. A length of stay of a target admission may be predicted based on the weighted confidence levels.

Abstract: Methods and apparatus for detecting anomalies in electronic data are described. An example apparatus includes a signature generator to generate a signature of context information for electronic transactions and to query a memorybase of previously received electronic transactions to identify a first entity associated with a subset of historical transactions that are associated with the signature, a neighborhood generator to query the memorybase to generate a neighborhood for the first entity, a target category identifier to determine a target category value for entities included in the neighborhood, and an anomaly detector to determine a score for the first entity based on the target category value and to present an alert indicating that the first entity is anomalous based on the score.

Abstract: Systems, apparatuses and methods may provide for technology that assigns confidence levels to data bins containing similarity data and length of stay data, wherein the similarity data and the length of stay data correspond to a plurality of previous admissions. Additionally, the confidence levels may be weighted based on a distribution metric that assigns higher weights to denser regions. A length of stay of a target admission may be predicted based on the weighted confidence levels.

Abstract: Methods and apparatus for detecting anomalies in electronic data are described. An example apparatus includes a signature generator to generate a signature of context information for electronic transactions and to query a memorybase of previously received electronic transactions to identify a first entity associated with a subset of historical transactions that are associated with the signature, a neighborhood generator to query the memorybase to generate a neighborhood for the first entity, a target category identifier to determine a target category value for entities included in the neighborhood, and an anomaly detector to determine a score for the first entity based on the target category value and to present an alert indicating that the first entity is anomalous based on the score.