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1.发明申请公开(公告)号：US20190272925A1
公开(公告)日：2019-09-05
申请号：US16290820
申请日：2019-03-01
发明人： Meredith A. Barrett , Guangquan Su , Michael J. Tuffli , Michael Lohmeier , Robert Austin Lee , Christopher Hogg , John David Van Sickle , Gregory F. Tracy , Nicholas John Hirons
摘要： A respiratory disease analytics system provides respiratory disease risk reports to a patient, provider, or third-party entity describing a patient's risk of experiencing a medication usage event given data in a geographic region. Regional data, including air pollutant conditions, weather conditions, demographic information, built environment factors, and regional health conditions for a geographic region are accessed from other sources and assigned based on event data recorded during a medicament usage event, as collected by sensors associated with the patient's medicament device/s. The regional data is assigned to medicament usage events occurring within a period of time. The assigned regional data is analyzed to determine an expected number of medication usage events for the geographic region occurring over the period of time.
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公开(公告)号：US11295862B2
公开(公告)日：2022-04-05
申请号：US16904508
申请日：2020-06-17
发明人： Guangquan Su , Meredith Ann Barrett , Olivier Humblet , Chris Hogg , John David Van Sickle , Kelly Anne Henderson , Gregory F. Tracy
摘要： An application server predicts respiratory disease risk, rescue medication usage, exacerbation, and healthcare utilization using trained predictive models. The application server includes model modules and submodel modules, which communicate with a database server, data sources, and client devices. The submodel modules train submodels by determining submodel coefficients based on training data from the database server. The submodel modules further determine statistical analysis data and estimates for medication usage events, healthcare utilization, and other related events. The model modules combine submodels to predict respiratory disease risk, exacerbation, rescue medication usage, healthcare utilization, and other related information. Model outputs are provided to users, including patients, providers, healthcare companies, electronic health record systems, real estate companies and other interested parties.
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公开(公告)号：US20220172845A1
公开(公告)日：2022-06-02
申请号：US17676053
申请日：2022-02-18
发明人： Guangquan Su , Meredith Ann Barrett , Olivier Humblet , Chris Hogg , John David Van Sickle , Kelly Anne Henderson , Gregory F. Tracy
摘要： An application server predicts respiratory disease risk, rescue medication usage, exacerbation, and healthcare utilization using trained predictive models. The application server includes model modules and submodel modules, which communicate with a database server, data sources, and client devices. The submodel modules train submodels by determining submodel coefficients based on training data from the database server. The submodel modules further determine statistical analysis data and estimates for medication usage events, healthcare utilization, and other related events. The model modules combine submodels to predict respiratory disease risk, exacerbation, rescue medication usage, healthcare utilization, and other related information. Model outputs are provided to users, including patients, providers, healthcare companies, electronic health record systems, real estate companies and other interested parties.
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公开(公告)号：US20160314256A1
公开(公告)日：2016-10-27
申请号：US15136667
申请日：2016-04-22
发明人： Guangquan Su , Meredith Ann Barrett , Olivier Humblet , Chris Hogg , John David Van Sickle , Kelly Anne Henderson , Gregory F. Tracy
摘要： An application server predicts respiratory disease risk, rescue medication usage, exacerbation, and healthcare utilization using trained predictive models. The application server includes model modules and submodel modules, which communicate with a database server, data sources, and client devices. The submodel modules train submodels by determining submodel coefficients based on training data from the database server. The submodel modules further determine statistical analysis data and estimates for medication usage events, healthcare utilization, and other related events. The model modules combine submodels to predict respiratory disease risk, exacerbation, rescue medication usage, healthcare utilization, and other related information. Model outputs are provided to users, including patients, providers, healthcare companies, electronic health record systems, real estate companies and other interested parties.
摘要翻译： 应用服务器使用训练有素的预测模型预测呼吸系统疾病风险，抢救药物使用情况，恶化和医疗保健利用。 应用服务器包括与数据库服务器，数据源和客户端设备通信的模型模块和子模块模块。 子模块模块通过基于来自数据库服务器的训练数据确定子模型系数来训练子模型。 子模型模块进一步确定用于药物使用事件，医疗保健利用和其他相关事件的统计分析数据和估计。 模型模块结合子模型预测呼吸系统疾病风险，恶化，救援药物使用，医疗保健利用等相关信息。 向用户提供模型输出，包括患者，提供者，医疗保健公司，电子健康记录系统，房地产公司和其他有关方面。
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公开(公告)号：US20200321127A1
公开(公告)日：2020-10-08
申请号：US16904508
申请日：2020-06-17
发明人： Guangquan Su , Meredith Ann Barrett , Oliver Humblet , Christopher Hogg , John David Van Sickle , Kelly Anne Henderson , Gregory Tracy
摘要： An application server predicts respiratory disease risk, rescue medication usage, exacerbation, and healthcare utilization using trained predictive models. The application server includes model modules and submodel modules, which communicate with a database server, data sources, and client devices. The submodel modules train submodels by determining submodel coefficients based on training data from the database server. The submodel modules further determine statistical analysis data and estimates for medication usage events, healthcare utilization, and other related events. The model modules combine submodels to predict respiratory disease risk, exacerbation, rescue medication usage, healthcare utilization, and other related information. Model outputs are provided to users, including patients, providers, healthcare companies, electronic health record systems, real estate companies and other interested parties.
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公开(公告)号：US10726954B2
公开(公告)日：2020-07-28
申请号：US15136667
申请日：2016-04-22
发明人： Guangquan Su , Meredith Ann Barrett , Olivier Humblet , Chris Hogg , John David Van Sickle , Kelly Anne Henderson , Gregory F. Tracy
摘要： An application server predicts respiratory disease risk, rescue medication usage, exacerbation, and healthcare utilization using trained predictive models. The application server includes model modules and submodel modules, which communicate with a database server, data sources, and client devices. The submodel modules train submodels by determining submodel coefficients based on training data from the database server. The submodel modules further determine statistical analysis data and estimates for medication usage events, healthcare utilization, and other related events. The model modules combine submodels to predict respiratory disease risk, exacerbation, rescue medication usage, healthcare utilization, and other related information. Model outputs are provided to users, including patients, providers, healthcare companies, electronic health record systems, real estate companies and other interested parties.
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