Abstract: A method for archiving biomedical data generated by a data collection device, includes the steps of automatically determining a data format in which the collection device is configured to store the biomedical data onto a computer-readable storage medium, based on the data format, extracting the biomedical data from the storage medium, and transmitting the extracted data to a database in which the extracted data is archived.

Abstract: An automated patient-management system for efficiently reporting patient information to clinicians, and a method of operation thereof is described. The system automatically collects and analyzes historic-physiologic data indicative of a patient's health from a myriad of sources. Based on the historic-physiologic data, the system automatically generates a health-risk score indicative of whether a patient will experience a serious-medical event in the near future (e.g., a predetermined time interval). The system uses a processing model that may rely on Hierarchical-Temporal Memory techniques and other models, for extracting and analyzing patient data. The system also generates an integrated synopsis of a patient's health condition, including the health-risk score. The synopsis is then presented (served) to a clinician in an organized, simplified, and effective manner via a client-side user interface.

Abstract: A method for archiving biomedical data generated by a data collection device, includes the steps of automatically determining a data format in which the collection device is configured to store the biomedical data onto a computer-readable storage medium, based on the data format, extracting the biomedical data from the storage medium, and transmitting the extracted data to a database in which the extracted data is archived.

Abstract: Systems and methods are provided for predicting rare events, such as hospitalization events. A set of data records, each containing multiple attributes with one or more values (which may include an “unknown” value), may represent a root node of a decision tree. This root node may be partitioned based on one of the attributes, such that the concentration (e.g., “purity”) of a relevant outcome (e.g., the rare event) is increased in one node and decreased in another. This process may be repeated until a decision tree with sufficiently pure leaf nodes is created. This “purified” decision tree may then be used to predict one or more rare events.