Invention Grant
- Patent Title: Population-level gaussian processes for clinical time series forecasting
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Application No.: US17441089Application Date: 2020-03-18
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Publication No.: US12087444B2Publication Date: 2024-09-10
- Inventor: Yale Chang , Bryan Conroy
- Applicant: KONINKLIJKE PHILIPS N.V.
- Applicant Address: NL Eindhoven
- Assignee: KONINKLIJKE PHILIPS N.V.
- Current Assignee: KONINKLIJKE PHILIPS N.V.
- Current Assignee Address: NL Eindhoven
- International Application: PCT/EP2020/057452 2020.03.18
- International Announcement: WO2020/187987A 2020.09.24
- Date entered country: 2021-09-20
- Main IPC: G16H50/20
- IPC: G16H50/20 ; G16H10/60 ; G16H50/30 ; G16H50/70

Abstract:
A device, system and method for generating a prediction model for a test patient. To generate the prediction model, a multi-dimensional clinical time series for each of a plurality of training patients is collected to generate a training population. A machine learning algorithm is then trained using the training population. Measurement data corresponding to the test patient is also received, the measurement data includes a multi-dimensional clinical time series for the test patient. The test patient is not included in the plurality of training patients. The prediction model is generated for the test patient based on i) the measurement data corresponding to the test patient and ii) training the machine learning algorithm using the training population.
Public/Granted literature
- US20220165417A1 POPULATION-LEVEL GAUSSIAN PROCESSES FOR CLINICAL TIME SERIES FORECASTING Public/Granted day:2022-05-26
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