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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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2.发明申请公开(公告)号：US20230087969A1
公开(公告)日：2023-03-23
申请号：US18071592
申请日：2022-11-29
Applicant: Geisinger Clinic
Inventor： Brandon K. Fornwalt , Christopher Haggerty , Linyuan Jing
IPC: G16H50/20 , G16H10/60 , G16H50/30 , G16H15/00 , G16H10/20 , G16H50/70 , A61B5/00 , G16H40/20 , G06N20/00 , A61B8/08 , G16H50/50 , A61B5/318
Abstract: A method for providing treatment recommendations for a patient to a physician is disclosed. The method includes receiving health information associated with the patient, determining a first risk score for the patient based on the health information using a trained predictor model, determining a second risk score for the patient based on the health information and at least one artificially closed care gap included in the health information using the predictor model, determining a predicted risk reduction score based on the first risk score and the second risk score, determining a patient classification based on the predicted risk reduction score, and outputting a report based on at least one of the first risk score, the second risk score, or the predicted risk reduction score.
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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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公开(公告)号：US20210076960A1
公开(公告)日：2021-03-18
申请号：US17026092
申请日：2020-09-18
Applicant: Tempus Labs , Geisinger Clinic
Inventor： Brandon K. Fornwalt , Christopher Haggerty , Shushravya Raghunath , Christopher Good , John Pfeifer , Alvaro Ulloa , Arun Nemani , Tanner Carbonati , Ashraf Hafez
Abstract: A method and system for predicting the likelihood that a patient will suffer from atrial fibrillation 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 atrial fibrillation 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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Patent Agency Ranking
6.发明申请METHOD AND SYSTEM FOR DISTRIBUTING PATIENT DATA AND PATIENT STATUS NOTIFICATIONS 审中-公开用于分配患者数据和患者状态通知的方法和系统公开(公告)号：US20150073829A1
公开(公告)日：2015-03-12
申请号：US14448211
申请日：2014-07-31
Applicant: Geisinger Clinic
Inventor： Eric D. Newman , James B. Jones , Virginia R. Lerch , Jonathan Billet
IPC: G06F19/00
Abstract: A method for distributing patient data includes: storing patient data entries, each entry including a patient identifier and patient data points, each point being associated with an access control; storing a plurality of user data entries, each user entry including a user identifier, authentication information, and access control information; receiving questionnaire answers and a specific patient identifier associated with the patient; updating the points in a specific patient entry including the specific patient identifier based on the questionnaire answers; receiving a patient data request including the specific patient identifier, a specific user identifier, and supplied authentication information; and transmitting a subset of points based on the access control associated with each point in the subset and the access control information included in a specific user data entry including the specific user identifier, if the supplied authentication information matches the authentication information included in the specific user data entry.
Abstract translation: 一种用于分发患者数据的方法包括：存储患者数据条目，每个条目包括患者标识符和患者数据点，每个点与访问控制相关联; 存储多个用户数据条目，每个用户条目包括用户标识符，认证信息和访问控制信息; 接收问卷答案和与患者相关联的特定患者标识符; 基于问卷答案更新包括特定患者标识符的特定患者条目中的点; 接收包括特定患者标识符，特定用户标识符和提供的认证信息的患者数据请求; 以及如果所提供的认证信息与所述特定用户中包括的认证信息匹配，则基于与所述子集中的每个点相关联的访问控制以及包括在包括所述特定用户标识符的特定用户数据条目中的访问控制信息来发送点的子集 数据输入。
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7.发明授权公开(公告)号：US12176111B2
公开(公告)日：2024-12-24
申请号：US18071592
申请日：2022-11-29
Applicant: Geisinger Clinic
Inventor： Brandon K. Fornwalt , Christopher Haggerty , Linyuan Jing
IPC: G16H50/20 , A61B5/00 , A61B8/08 , G06N20/00 , G16H10/20 , G16H10/60 , G16H15/00 , G16H40/20 , G16H50/30 , G16H50/50 , G16H50/70 , A61B5/33
Abstract: A method for providing treatment recommendations for a patient to a physician is disclosed. The method includes receiving health information associated with the patient, determining a first risk score for the patient based on the health information using a trained predictor model, determining a second risk score for the patient based on the health information and at least one artificially closed care gap included in the health information using the predictor model, determining a predicted risk reduction score based on the first risk score and the second risk score, determining a patient classification based on the predicted risk reduction score, and outputting a report based on at least one of the first risk score, the second risk score, or the predicted risk reduction score.
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公开(公告)号：US11869668B2
公开(公告)日：2024-01-09
申请号：US17829357
申请日：2022-05-31
Applicant: Tempus Labs, Inc. , Geisinger Clinic
Inventor： Arun Nemani , Greg Lee , Steve Steinhubl , Alvaro Ulloa-Cerna
CPC classification number: G16H50/30 , A61B5/0006 , A61B5/28 , A61B5/318 , A61B5/7275 , G16H50/20 , G06F18/2155
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：US11957507B2
公开(公告)日：2024-04-16
申请号：US17099746
申请日：2020-11-16
Applicant: Geisinger Clinic
Inventor： Brandon K. Fornwalt , Christopher Haggerty , Alvaro Ulloa-Cerna , Christopher Good
IPC: A61B8/00 , A61B8/04 , A61B8/06 , A61B8/08 , A61B8/14 , G06N3/045 , G06N3/08 , G06T7/00 , G16H10/40 , G16H10/60 , G16H15/00 , G16H20/40 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/70 , G16H70/60
CPC classification number: A61B8/0883 , A61B8/04 , A61B8/065 , A61B8/14 , A61B8/5223 , A61B8/5246 , A61B8/5292 , G06N3/045 , G06N3/08 , G06T7/0002 , G06T7/0012 , G16H10/40 , G16H10/60 , G16H15/00 , G16H20/40 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/70 , G16H70/60 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30048
Abstract: A method for determining a predicted risk level of a clinical endpoint for a predetermined time period for a patient is provided by the present disclosure. The method includes receiving video frames of a heart, the video frames being associated with the patient, receiving electronic health record data including a number of variables associated with the patient, providing the video frames and the electronic health record data to the trained neural network, receiving a risk score from the trained neural network, and outputting a report based on the risk score to at least one of a display or a memory.
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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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