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    PREDICTIVE MODELING OF RESPIRATORY DISEASE RISK AND EVENTS
    1.
    发明申请
    PREDICTIVE MODELING OF RESPIRATORY DISEASE RISK AND EVENTS 有权

    公开(公告)号：US20220172845A1

    公开(公告)日：2022-06-02

    申请号：US17676053

    申请日：2022-02-18

    申请人： Reciprocal Labs Corporation (dba Propeller Health)

    发明人： Guangquan Su ,  Meredith Ann Barrett ,  Olivier Humblet ,  Chris Hogg ,  John David Van Sickle ,  Kelly Anne Henderson ,  Gregory F. Tracy

    IPC分类号： G16H50/20 ,  G06N20/00 ,  G16H20/13

    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.

    Device and Method to Monitor, Track, Map and Analyze Usage of Metered-Dose Inhalers in Real-Time
    2.
    发明申请
    Device and Method to Monitor, Track, Map and Analyze Usage of Metered-Dose Inhalers in Real-Time 审中-公开
    监控，跟踪，映射和分析计量吸入器实时使用的设备和方法

    公开(公告)号：US20170072144A1

    公开(公告)日：2017-03-16

    申请号：US15364174

    申请日：2016-11-29

    申请人： Reciprocal Labs Corporation (dba Propeller Health)

    发明人： John David Van Sickle ,  Gregory F. Tracy

    IPC分类号： A61M15/00 ,  G06F19/00

    CPC分类号： A61M15/008 ,  A61M15/00 ,  A61M2205/3306 ,  A61M2205/3317 ,  A61M2205/3553 ,  A61M2205/3592 ,  A61M2205/6027 ,  A61M2205/6054 ,  A61M2205/6063 ,  A61M2209/01 ,  G06F19/3418 ,  G06F19/3462

    摘要： A system and method for accurately and reliably determining and recording the time, date and location where a medication is used, and a system and method for transmitting, collecting, and using that data to improve clinical care, disease management, and public health surveillance. The device allows information concerning drug usage, including the time, date and location of use, to be transmitted to a remote network computer system so that the data can be evaluated to determine current impairment and future risk, and to identify changes in the frequency, timing, or location of medication usage indicative of change in disease control or management, and to examine spatial, temporal or demographic patterns of medication use or absence of use among individuals and groups. In addition, the device may further be configured to transmit signals indicative of its status, condition or other results to the remote network computer system.

    摘要翻译： 准确可靠地确定和记录使用药物的时间，日期和位置的系统和方法，以及用于传输，收集和使用该数据以改进临床护理，疾病管理和公共卫生监测的系统和方法。 该设备允许将关于药物使用的信息（包括时间，日期和使用位置）发送到远程网络计算机系统，以便可以评估数据以确定当前的损害和未来的风险，并且识别频率的变化， 时间安排或用于指示疾病控制或管理变化的药物用途的位置，以及检查个体和组之间药物使用或不使用的空间，时间或人口统计学模式。 另外，该设备还可被配置为向远程网络计算机系统发送指示其状态，条件或其他结果的信号。

    PREDICTIVE MODELING OF RESPIRATORY DISEASE RISK AND EVENTS
    8.
    发明申请
    PREDICTIVE MODELING OF RESPIRATORY DISEASE RISK AND EVENTS 审中-公开

    公开(公告)号：US20200321127A1

    公开(公告)日：2020-10-08

    申请号：US16904508

    申请日：2020-06-17

    申请人： Reciprocal Labs Corporation (dba Propeller Health)

    发明人： Guangquan Su ,  Meredith Ann Barrett ,  Oliver Humblet ,  Christopher Hogg ,  John David Van Sickle ,  Kelly Anne Henderson ,  Gregory Tracy

    IPC分类号： G16H50/20 ,  G06N20/00

    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.