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公开(公告)号：US11756688B2
公开(公告)日：2023-09-12
申请号：US17829351
申请日：2022-05-31
Applicant: Tempus Labs, Inc. , Geisinger Clinic
Inventor： Alvaro E. Ulloa-Cerna , Noah Zimmerman , Greg Lee , Christopher M. Haggerty , Brandon K. Fornwalt , Ruijun Chen , John Pfeifer , Christopher Good
CPC classification number: G16H50/30 , A61B5/0006 , A61B5/28 , A61B5/318 , A61B5/7275 , G16H50/20 , G06F18/2155
Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.
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Patent Agency Ranking
公开(公告)号：US20230148456A9
公开(公告)日：2023-05-11
申请号：US17829356
申请日：2022-05-31
Applicant: Tempus Labs, Inc.
Inventor： Noah Zimmerman , Brandon Fornwalt , John Pfeifer , Ruijun Chen , Arun Nemani , Greg Lee , Steve Steinhubl , Christopher Haggerty , Sushravya Raghunath , Alvaro Ulloa-Cerna , Linyuan Jing , Thomas Morland
CPC classification number: A61B5/7275 , A61B5/318 , G16H50/30 , G16H50/20
Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.
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公开(公告)号：US11657921B2
公开(公告)日：2023-05-23
申请号：US17829356
申请日：2022-05-31
Applicant: Tempus Labs, Inc. , Geisinger Clinic
Inventor： Noah Zimmerman , Brandon Fornwalt , John Pfeifer , Ruijun Chen , Arun Nemani , Greg Lee , Steve Steinhubl , Christopher Haggerty , Sushravya Raghunath , Alvaro Ulloa-Cerna , Linyuan Jing , Thomas Morland
CPC classification number: G16H50/30 , A61B5/0006 , A61B5/28 , A61B5/318 , A61B5/7275 , G16H50/20 , G06K9/6259
Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.
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公开(公告)号：US20220384044A1
公开(公告)日：2022-12-01
申请号：US17829351
申请日：2022-05-31
Applicant: Tempus Labs, Inc. , Geisinger Clinic
Inventor： Alvaro E. Ulloa-Cerna , Noah Zimmerman , Greg Lee , Christopher M. Haggerty , Brandon K. Fomwalt , Ruijun Chen , John Pfeifer , Chris Good
Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.
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公开(公告)号：US20230343464A1
公开(公告)日：2023-10-26
申请号：US18342852
申请日：2023-06-28
Applicant: Tempus Labs, Inc. , Geisinger Clinic
Inventor： Alvaro E. Ulloa-Cerna , Noah Zimmerman , Greg Lee , Christopher M. Haggerty , Brandon K. Fornwalt , Ruijun Chen , John Pfeifer , Christopher Good
CPC classification number: G16H50/30 , G16H50/20 , A61B5/318 , A61B5/7275 , A61B5/28 , A61B5/0006 , G06F18/2155
Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.
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公开(公告)号：US20230245782A1
公开(公告)日：2023-08-03
申请号：US18299049
申请日：2023-04-12
Applicant: Tempus Labs, Inc. , Geisinger Clinic
Inventor： Noah Zimmerman , Brandon Fornwalt , John Pfeifer , Ruijun Chen , Arun Nemani , Greg Lee , Steve Steinhubl , Christopher Haggerty , Sushravya Raghunath , Alvaro Ulloa-Cerna , Linyuan Jing , Thomas Morland
CPC classification number: G16H50/30 , G16H50/20 , A61B5/318 , A61B5/7275 , A61B5/28 , A61B5/0006 , G06F18/2155
Abstract: A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One or more leads of the plurality of leads that are derivable from a combination of other leads of the plurality of leads are identified, and a portion of the electrocardiogram trace data does not include electrocardiogram trace data of the one or more leads. The portion of the electrocardiogram data is provided to a trained machine learning model, to evaluate the portion of the electrocardiogram trace data with respect to one or more cardiac disease states. A risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time is generated by the trained machine learning model based on the evaluation.
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