General Electric Company

Ge Precision Healthcare LLC

Bex George Thomas

Andrew Day

Christopher Donald Johnson

Srinivas Bollapragada

Savanoor Pradeep Rai

Kunter Seref Akbay

David Toledano

Rajesh Tyagi

Peter Katlic

Hong Yang

Dianne Chace

Ahmed Elasser

Jason Wayne Black

Mohammed Agamy

Anthony William Galbraith

Edward McQuillan

Ilkin Onur Dulgeroglu

John Andrew Ellis

Nitish Umang

Maxim Valeryevich Garifullin

Ryan Mancl

Rulin Chen

Leonardo Dias

Abram Christopher Friesen

Gregory Peter Betman

Vijay K Veeraghattam

Aristotelis Emmanouil Thanos Filis

Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can include a grouping component that defines a group of beds at a medical facility based on at least one grouping factor, and a group stability component that determines a measure of occupancy variability for the group based on historical census data for respective beds in the group. The system can further include a model selection component that selects one or more census forecasting models for the group based on the measure of occupancy variability, and a patient census component that applies the one or more census forecasting models to current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time.

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

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.

Systems and methods for multi-resource scheduling are disclosed and described. An example apparatus includes a scheduler engine configured to enable clinical system(s) to operate with the scheduler engine in an analytical mode and an operating mode. When in the analytical mode, the scheduler engine is to dynamically calculate one or more binding constraints on the one or more clinical systems for scheduling. When in the operating mode, the scheduler engine is to manage and output a schedule for the one or more clinical systems based on the one or more binding constraints calculated in the analytical mode. The example scheduler engine is to dynamically switch between the analytical mode and the operating mode based at least in part on a probabilistic determination of delay associated with the schedule.

Systems and methods for multi-resource scheduling

Display screen or portion thereof with a graphical user interface

Display screen or portion thereof with graphical user interface

Systems and methods are provided to schedule clinical tasks involving multiple sub-tasks and multiple resources in a clinical enterprise. An example method includes identifying a slot for a task defined by a task duration and one or more resources, the task including a plurality of sub-tasks, each sub-task having a sub-task duration utilizing one or more of the one or more resources, wherein each sub-task to be performed consecutively based on resource constraints; selecting a time slot for the task based on resource availability, the plurality of sub-tasks in the task, and a duration associated with each sub-task, wherein resource availability information is obtained from a clinical information system, and wherein each resource is scheduled only for one or more sub-tasks in which the resource is involved; displaying the schedule including the task and the plurality of sub-tasks; and facilitating access to view and modify the schedule.

A method and system for controlling demand events in a utility network with multiple customer sites. The value of a demand response parameter threshold for invoking a demand response event is calculated based on the number of available demand response events and the number of opportunities remaining to issue the available demand response events. This parameter represents the utility objectives for using the demand response program (e.g., cost savings, reliability, avoided costs). A current value of the demand response parameter is compared to the threshold value, and a determination is made whether or not to call a demand response event for the current opportunity, or to save the event for a future opportunity based upon this comparison.

Method and system for demand response management in a network

A system and method of using one or more DC-DC/DC-AC converters and/or alternative devices allows strings of multiple module technologies to coexist within the same PV power plant. A computing (optimization) framework estimates the percentage allocation of PV power plant capacity to selected PV module technologies. The framework and its supporting components considers irradiation, temperature, spectral profiles, cost and other practical constraints to achieve the lowest levelized cost of electricity, maximum output and minimum system cost. The system and method can function using any device enabling distributed maximum power point tracking at the module, string or combiner level.

System and method for design and optimization of grid connected photovoltaic power plant with multiple photovoltaic module technologies

Systems and methods for determining how to stack containers/trailers on a vehicle consist at a terminal/yard at least to maintain aerodynamic efficiency. Embodiments of the present invention provide a terminal management software application configured to determine how to stack containers/trailers on vehicles of a vehicle consist, taking into account a resultant aerodynamic efficiency of the vehicle consist during transit as well as other factors.

Train profile evaluation system and method

Growing community of inventors

Laguna Niguel, CA, United States of America

Bex George Thomas