利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

15 条记录 (耗时 0.019 秒)

    Apparatus and method for prediction and management of participant compliance in clinical research
    9.
    发明申请
    Apparatus and method for prediction and management of participant compliance in clinical research 有权
    临床研究参与者依从性预测与管理的装置及方法

    公开(公告)号：US20060184493A1

    公开(公告)日：2006-08-17

    申请号：US11324504

    申请日：2006-01-03

    申请人： Saul Shiffman ,  Michael Hufford ,  Jean Paty

    发明人： Saul Shiffman ,  Michael Hufford ,  Jean Paty

    IPC分类号： G06N5/02

    CPC分类号： G06Q90/00 ,  G06F19/00 ,  G06Q10/04 ,  G16H10/20 ,  G16H40/20

    摘要： A system for developing and implementing empirically derived algorithms to generate decision rules to determine participant noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict participant noncompliance and fraud with research protocol, including performance and enrollment goals, in the clinical trial. The data may be used to overall predict the performance of any participant in a clinical trial, allowing selection of participants that tend to produce useful, high-quality results. The present invention can also be used to monitor participant compliance with the research protocol and goals to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring participant removal from the clinical trial or from future clinical trials. The algorithms and decision rules can also be domain-specific, such as detecting non-compliance or fraud among subjects in a cardiovascular drug trial, or demographically specific, such as taking into account gender, age or location, which provides for algorithms and decision rules to be optimized for the specific sample of participants being studied.

    摘要翻译： 用于开发和实施经验派生算法以生成决策规则以确定临床试验中的研究协议的参与者违规和欺诈的系统允许通过研究方案来识别检测或预测参与者违规和欺诈的复杂变量模式，包括绩效和注册 目标，在临床试验。 数据可用于总体预测临床试验中任何参与者的表现，允许选择倾向于产生有用的高质量结果的参与者。 本发明还可以用于监视参与者遵守研究协议和目标以确定要执行的优选动作。 任选地，本发明可以从仅需要纠正反馈的轻微不合规性到要求参与者从临床试验或未来临床试验中移除的重大不合规提供一系列不合规。 算法和决策规则也可以是域特定的，例如在心血管药物试验中检测受试者的不合规或欺诈，或人口统计特异性，例如考虑性别，年龄或位置，其提供算法和决策规则 针对正在研究的参与者的具体样本进行优化。

    Operation and method for prediction and management of the validity of subject reported data
    10.
    发明授权
    Operation and method for prediction and management of the validity of subject reported data 有权
    预测和管理受试者报告数据有效性的操作和方法

    公开(公告)号：US09129215B2

    公开(公告)日：2015-09-08

    申请号：US12965719

    申请日：2010-12-10

    申请人： Saul Shiffman ,  Douglas R. Engfer ,  Jean Paty

    发明人： Saul Shiffman ,  Douglas R. Engfer ,  Jean Paty

    IPC分类号： G06F1/00 ,  G06N5/00 ,  G06N5/02

    CPC分类号： G06F17/30539 ,  G06F17/30528 ,  G06N5/02 ,  G06N5/025

    摘要： A system for developing and implementing empirically derived algorithms to generate decision rules to predict invalidity of subject reported data and fraud with research protocols in surveys allows for the identification of complex patterns of variables that detect or predict subject invalidity of subject reported data and fraud with the research protocol in the survey. The present invention may also be used to monitor invalidity of subject reported data within a research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of invalidity, from minor invalidity needing only corrective feedback, to significant invalidity requiring subject removal from the survey. The algorithms and decision rules can also be domain-specific, such as detecting invalidity or fraud among subjects in a workplace satisfaction survey, or demographically specific, such as taking into account gender or age. The algorithms and decision rules may be optimized for the specific sample of subjects being studied.

    摘要翻译： 用于开发和实施经验导出的算法以生成决策规则以通过调查研究方案预测受试者报告数据和欺诈的无效性的系统允许识别检测或预测主题报告数据和欺诈的主体无效的变量的复杂模式 研究方案在调查中。 本发明还可用于监测研究协议内被检体报告数据的无效性，以确定要执行的优选动作。 任选地，本发明可以提供一系列无效性，从仅需要校正反馈的轻微无效，到需要从调查中移除问题的显着无效。 算法和决策规则也可以是域特定的，例如在工作场所满意度调查中检测主体之间的无效或欺诈，或者人口特异性，例如考虑到性别或年龄。 可以针对正在研究的受试者的具体样本优化算法和决策规则。