Koninklijke Philips Corporation N.v.

Reza Sharifi Sedeh

Yugang Jia

Eran Simhon

Amir Mohammad Tahmasebi Maraghoosh

Murray Fulton Gillies

Daniel Robert Elgort

Daisy Van Minde

Gertjan Laurens Schuurkamp

Eran Rubens

Xin Wang

Cecilia Meijer

Jordan Lento

Yang Yang

Amir Abdolahi

Wei Wang

Methods and systems for selecting a treatment for a patient. The system extracts an incidental finding from a record associated with a patient and an associated follow-up recommendation. The system then determines whether any inconsistencies exist between the follow-up recommendation from the report and a follow-up recommendation prescribed by institutional or administrative guidelines. Any inconsistencies may then be resolved to guarantee an appropriate workflow for patient care.

Selecting a treatment for a patient

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

According to an aspect, there is provided an apparatus for use in detecting malingering by a first subject in a test of physical and/or mental function of the first subject. The apparatus comprises a memory unit configured to store a plurality of models, each model identifies a respective set of physical and/or mental function tests from a plurality of different physical and/or mental function tests that can be used to detect malingering in a subject and that meets one or more possible user requirements for detecting malingering; and a processing unit configured to receive a user input from a user of the apparatus, the received user input indicating a user requirement for detecting malingering in the first subject; following receipt of an indication that the first subject may be malingering in one or more physical and/or mental function tests, retrieve, from the memory unit, the identity of a first set of physical and/or mental function tests for a model that meets the indicated user requirement; and output a control signal to a function testing device indicating the retrieved identity of the first set of physical and/or mental function tests such that the testing device provides the identified first set of physical and/or mental function tests to the first subject completion for completion.

Method and apparatus for use in detecting malingering by a first subject in tests of physical and/or mental function of the first subject

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

The present disclosure pertains to a system configured to generate computer simulations of patient loads for units of health care facilities. The system is configured to: obtain past patient census information for an individual unit of a health care facility, the past patient census information comprising a quantity of patient visits to the individual unit during past periods of time; determine intra-period variation and inter-period variation in the quantity of patient visits to the individual unit during the past periods of time; classify the individual unit based on the intra-period and inter-period variations; and generate a computer simulation of patient loads for the individual unit based on the classification. The computer-simulated patient loads comprise a quantity of patient visits to the individual unit during one or more future periods of time. The computer simulation is performed using a non-parametric simulation algorithm.

Health care facility unit computer simulation system

A database merger method () merges two or more anonymized healthcare databases (X, Y). Each anonymized healthcare database has personally identifying information anonymized including having medical care units replaced by medical care unit placeholders. In the database merger method, statistical patient feature distributions are computed for medical care unit placeholders in the anonymized healthcare databases. Medical care unit placeholders in different anonymized healthcare databases are matched by matching corresponding statistical patient feature distributions for the respective medical care unit placeholders. Patients in different anonymized healthcare databases are matched. The patient matching is performed within matched pairs of medical care unit placeholders to improve computational efficiency. A databases alignment table () is generated identifying the matched patients, or a merged anonymized healthcare database is generated that merges patient features in the different anonymized healthcare databases for each matched patient into a single patient entry.

Growing community of inventors

Malden, MA, United States of America

Reza Sharifi Sedeh