General Electric Company

Ge Precision Healthcare LLC

Savanoor Pradeep Rai

Andrew Day

Bex George Thomas

Rajesh Tyagi

Nitish Umang

Ryan Mancl

Hong Yang

Rulin Chen

Aristotelis Emmanouil Thanos Filis

Leonardo Dias

Techniques are described for managing tasks of a dynamic system with limited resources using a machine-learning and combinatorial optimization framework. In one embodiment, a computer-implemented method is provided that comprises employing, by a system operatively coupled to a processor, one or more first machine learning models to determine a total demand for tasks of a dynamic system within a defined time frame based on state information regarding a current state of the dynamic system, wherein the state information comprises task information regarding currently pending tasks of the tasks. The method further comprises, employing, by the system, one or more second machine learning models to determine turnaround times for completing the tasks based on the state information, and determining, by the system, a prioritization order for performing the currently pending tasks based on the total demand and the turnaround times.

Machine-learning and combinatorial optimization framework for managing tasks of a dynamic system with limited resources

Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients. The method can further include providing discharge information identifying the discharge destinations predicted for the respective patients to one or more care providers to facilitate managing and coordinating inpatient and post-discharge care for the respective patients.

System and method for identifying complex patients, forecasting outcomes and planning for post discharge care

Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can perform a first machine learning process to learn patterns in patient flow data related to a set of patient identities and a set of operations associated with a set of medical inpatient units. The system can also perform a second machine learning process to detect abnormalities associated with the patterns in the patient flow data. Furthermore, the system can determine patient census data associated with a prediction for a total number of patient identities in the set of medical inpatient units during a period of time based on the patterns and the abnormalities. The system can also generate an alert for a user interface in response to a determination that the patient census data satisfies a defined criterion.

Monitoring, predicting and alerting for census periods in medical inpatient units

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Naperville, IL, United States of America

Savanoor Pradeep Rai