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公开(公告)号：US20210052217A1
公开(公告)日：2021-02-25
申请号：US16919154
申请日：2020-07-02
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Claire Yunzhu Zhao , Bryan Conroy , Mohammad Shahed Sorower , David Paul Noren , Kailash Swaminathan , Chaitanya Kulkarni , Ting Feng , Kristen Tgavalekos , Emma Holdrich Schwager , Erina Ghosh , Vinod Kumar , Vikram Shivanna , Srinivas Hariharan , Daniel Craig McFarlane
Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.
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公开(公告)号：US11925474B2
公开(公告)日：2024-03-12
申请号：US16919154
申请日：2020-07-02
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Claire Yunzhu Zhao , Bryan Conroy , Mohammad Shahed Sorower , David Paul Noren , Kailash Swaminathan , Chaitanya Kulkarni , Ting Feng , Kristen Tgavalekos , Emma Holdrich Schwager , Erina Ghosh , Vinod Kumar , Vikram Shivanna , Srinivas Hariharan , Daniel Craig McFarlane
CPC classification number: A61B5/4842 , A61B5/7225 , A61B5/742 , G16H10/60 , G16H50/20 , G16H50/30 , G16H50/50 , G16H50/70
Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.
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公开(公告)号：US10896763B2
公开(公告)日：2021-01-19
申请号：US16242350
申请日：2019-01-08
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Vinutha Kempanna , Srinivas Hariharan , Siripurapu Mahesh Reddy , Kiran Kumar Yadalam
Abstract: The present disclosure pertains to a system for providing model-based treatment recommendation via individual-specific machine learning models. In some embodiments, the system (i) obtains an audio recording of an individual, (ii) determines, from the audio recording, one or more utterance-related features of the individual; (iii) performs one or more queries based on the one or more utterance-related features to obtain health information (e.g., utterance-related conditions and treatments provided for the utterance-related conditions) associated with similar individuals having similar utterance-related conditions as the subject; (iv) provides the health information associated with the similar individuals to a machine learning model to train the machine learning model; and (v) provides, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.
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