公开(公告)号：US20190139631A1

公开(公告)日：2019-05-09

申请号：US16097299

申请日：2017-05-04

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Larry James ESHELMAN ,  Eric Thomas CARLSON ,  Lin YANG ,  Minnan XU ,  Bryan CONROY

IPC: G16H10/60 ,  G16H50/20 ,  G06N20/00

Abstract: The present disclosure relates to estimation and use of clinician assessment of patient acuity. In various embodiments, a plurality of patient feature vectors associated with a plurality of respective patients may be obtained (302, 304). Each patient feature vector may include one or more health indicator features indicative of observable health indicators of a patient, and one or more treatment features indicative of characteristics of treatment provided to the patient. A machine learning model (216) may be trained (306) based on the patient feature vectors to receive, as input, subsequent patient feature vectors, and to provide, as output, indications of levels of clinician acuity assessment. Later, a patient feature vector associated with a given patient may be provided (404) as input to the machine learning model. Based on output from the machine learning model, a level of clinician acuity assessment associated with the given patient may be estimated (406) and used (408-416) for various applications.

公开(公告)号：US20230049068A1

公开(公告)日：2023-02-16

申请号：US17879938

申请日：2022-08-03

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Eran SIMHON ,  Lasith ADHIKARI ,  Gregory BOVERMAN ,  David Paul NOREN ,  Chaitanya KULKARNI ,  Larry James ESHELMAN ,  Syamanthaka BALAKRISHNAN ,  Vikram SHIVANNA

IPC: G16H40/20

Abstract: A method and system for generating real time workload balancing recommendations comprising receiving transition data, medical data, and staffing data; determining a transition probability for each of a plurality of patients; determining a predicted workload to be generated by each of the plurality of patients; simulating the predicted workload to be generated by each of the plurality of patients, the future workload for each of a plurality of units in the hospital; generating staffing recommendations; and displaying the generated staffing recommendations on a user display of the workload balancing system.

公开(公告)号：US20170277853A1

公开(公告)日：2017-09-28

申请号：US15528564

申请日：2015-12-14

Applicant: Koninklijke Philips N.V.

Inventor： Eric Thomas CARLSON ,  Larry James ESHELMAN

IPC: G06F19/00

CPC classification number: G06F19/3418 ,  G16H50/30

Abstract: An early warning system for patient monitoring includes one or more patient monitors (620) configured to generate patient physiological data, a patient database (602) storing patient physiological measurements and outcomes, and one or more computer processors (604) programmed to: machine learn an Aggregate Weighted Track and Trigger System (AWTTS) algorithm for quantifying patient condition by an AWTTS score based on a training set of the patient physiological measurements and outcomes; apply an Early Warning Score or Modified Early Warning Score (EWS) algorithm to patient physiological measurements to generate EWS scores; apply the machine-learned AWTTS algorithm to the patient physiological measurements to generate AWTTS scores; and create a mapping between the AWTTS scores and the EWS scores.