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公开(公告)号：US20220375560A1
公开(公告)日：2022-11-24
申请号：US17882824
申请日：2022-08-08
Applicant: IQVIA Inc.
Inventor： Virupaxkumar Bonageri , Rajneesh Patil , Nithyanandan Thangavelu , Jian Huang , Vijay Pratap A
Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
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公开(公告)号：US20250124529A1
公开(公告)日：2025-04-17
申请号：US18989609
申请日：2024-12-20
Applicant: IQVIA Inc.
Inventor： Virupaxkumar Bonageri , Rajneesh Patil , Nithyanandan Thangavelu , Jian Huang , Vijay Pratap A
Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
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公开(公告)号：US11526953B2
公开(公告)日：2022-12-13
申请号：US16451097
申请日：2019-06-25
Applicant: IQVIA Inc.
Inventor： Virupaxkumar Bonageri , Rajneesh Patil , Nithyanandan Thangavelu , Jian Huang , Vijay Pratap A
Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
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