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公开(公告)号：US20230148456A9
公开(公告)日：2023-05-11
申请号：US17829356
申请日：2022-05-31
申请人： Tempus Labs, Inc.
发明人： 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分类号： A61B5/7275 , A61B5/318 , G16H50/30 , G16H50/20
摘要： 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：US20230245782A1
公开(公告)日：2023-08-03
申请号：US18299049
申请日：2023-04-12
发明人： 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分类号： G16H50/30 , G16H50/20 , A61B5/318 , A61B5/7275 , A61B5/28 , A61B5/0006 , G06F18/2155
摘要： 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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公开(公告)号：US11657921B2
公开(公告)日：2023-05-23
申请号：US17829356
申请日：2022-05-31
发明人： 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分类号： G16H50/30 , A61B5/0006 , A61B5/28 , A61B5/318 , A61B5/7275 , G16H50/20 , G06K9/6259
摘要： 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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