公开(公告)号：US20210350933A1

公开(公告)日：2021-11-11

申请号：US17277619

申请日：2019-09-16

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： David Paul NOREN ,  Asif RAHMAN ,  Bryan CONROY ,  Minnan XU ,  Nikhil GALAGALI

IPC: G16H50/30 ,  G16H50/20 ,  G16H10/60 ,  G16H50/70 ,  G06N20/00 ,  G06F17/18 ,  A61B5/00

Abstract: Various embodiments of the present disclosure are directed to a general statistical classifier (40) and a personal statistical classifier (50) for executing a patient risk prediction method. In operation, the general statistical classifier (40) may render a singular general independent vital sign risk score for a singular vital sign and/or may render plural general independent vital sign risk scores for plural vital signs. The personal statistical classifier (50) may render a singular personal vital sign risk score from an integration of a singular patient feature into the singular general independent vital sign risk score, and/or may also render plural personal independent vital sign risk scores from individual integrations of plural patient features into the singular general independent vital sign risk score, individual integrations of a singular patient feature into the plural general independent vital sign risk scores, and/or individual integrations of plural patient features into the plural general independent vital sign risk scores.

公开(公告)号：US20220036055A1

公开(公告)日：2022-02-03

申请号：US17504575

申请日：2021-10-19

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Christine Menking SWISHER ,  Asif RAHMAN

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06K9/32

Abstract: Techniques disclosed herein relate to identifying individuals in digital images. In some embodiments, a digital image(s) that captures a scene containing one or more people may be acquired. The single digital image may be applied as input across a single machine learning model. In some implementations, the single machine learning model may be trained to perform a non-facial feature recognition task and a face-related recognition task. Output may be generated over the single machine learning model based on the input. The output may include first data indicative of non-facial features of a given person of the one or more people and second data indicative of at least a location of a face of the given person in the digital image relative to the non-facial features. In various embodiments, the given person may be identified based at least in part on the output.

公开(公告)号：US20220028565A1

公开(公告)日：2022-01-27

申请号：US17276708

申请日：2018-09-17

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Nikhil GALAGALI ,  Minnan XU ,  Bryan CONROY ,  Asif RAHMAN ,  David Paul NOREN

IPC: G16H70/60 ,  G16H10/60

Abstract: A method of determining patient subtyping from disease progression trajectories, including: extracting patient data and related time stamps from patient record data related to a disease, wherein the extracted patient data is incomplete and irregular; building a continuous-time disease progression model based upon the extracted patient data; and building a mixture model for clustering of patient disease trajectory subtypes.