Koninklijke Philips Corporation N.v.

Eran Simhon

Reza Sharifi Sedeh

Larry James Eshelman

Xin Wang

David Paul Noren

Gertjan Laurens Schuurkamp

Lasith Adhikari

Amir Abdolahi

Jordan Lento

Cecilia Meijer

Vikram Shivanna

Chaitanya Kulkarni

Gregory Boverman

Syamanthaka Balakrishnan

Disclosed herein is a system for generating real time staffing recommendations for a hospital. The system receives data, including transition data for multiple patients, medical data for the multiple patients, and staffing data. Using the data, the system determines a transition probability for each patient and a predicted workload for each patient. The system simulates unit workloads for multiple units in the hospital using the transition probability and predicted workload for each patient. The system generates staffing recommendations using the simulated unit workloads and displays the staffing recommendations on a user display.

Systems and methods for real time workload balancing

The present disclosure pertains to a system configured to prepare and use prediction models for socioeconomic data and missing value prediction. Some embodiments may: extract, from received population segment data, a training set of socioeconomic parameter values for each population segment; provide, to a prediction model as input, first parameter values of the respective training set for the prediction of additional parameter values of the training set such that the prediction of the additional parameter values is performed without reliance on the additional parameter values; provide, for each of the training sets, the additional parameter values to the prediction model as reference feedback for the prediction model's prediction of the additional parameter values to train the prediction model; and predict, based on a working set of parameter values for a population segment, additional values for the working set using the prediction model subsequent to its training.

Prediction model preparation and use for socioeconomic data and missing value prediction

The present system is configured to display distributions of predicted health outcome information for patient populations in geographical areas. The system is configured to obtain demographic, social (e.g., including environmental), and prior health outcome information for a patient population in a geographical area. The system is configured to train a prediction model based on the demographic, social, and prior health outcome information, which outputs weighted features of the demographic and social information that are predictive of health outcomes for the patient population. The system is configured to cause display of a distribution of predicted health outcome information for the patient population in the geographical area based on the weighted features.

System and method for prediction-model-based display of distributions of health outcome information for patient populations in geographical areas

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Boston, MA, United States of America

Eran Simhon