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    Rapid Learning Community for Predictive Models of Medical Knowledge
    3.
    发明申请
    Rapid Learning Community for Predictive Models of Medical Knowledge 审中-公开
    医学知识预测模型快速学习社区

    公开(公告)号：US20140088989A1

    公开(公告)日：2014-03-27

    申请号：US14027494

    申请日：2013-09-16

    申请人： Balaji Krishnapuram ,  Bharat R. Rao ,  Glenn Fung ,  Vikram Anand ,  Faisal Farooq ,  Wolfgang Wiessler ,  Shipeng Yu

    发明人： Balaji Krishnapuram ,  Bharat R. Rao ,  Glenn Fung ,  Vikram Anand ,  Faisal Farooq ,  Wolfgang Wiessler ,  Shipeng Yu

    IPC分类号： G06F19/00

    CPC分类号： G16H50/70 ,  G16H50/50

    摘要： A predictive model of medical knowledge is trained from patient data of multiple different medical centers. The predictive model is machine learnt from routine patient data from multiple medical centers. Distributed learning avoids transfer of the patient data from any of the medical centers. Each medical center trains the predictive model from the local patient data. The learned statistics, and not patient data, are transmitted to a central server. The central server reconciles the statistics and proposes new statistics to each of the local medical centers. In an iterative approach, the predictive model is developed without transfer of patient data but with statistics responsive to patient data available from multiple medical centers. To assure comfort with the process, the transmitted statistics may be in a human readable format.

    摘要翻译： 医学知识的预测模型是从多个不同医疗中心的患者数据进行培训。 预测模型是从多个医疗中心的常规患者数据获得的机器。 分布式学习避免了从任何医疗中心转移患者数据。 每个医疗中心从当地患者数据中训练预测模型。 学习的统计信息而不是患者数据被传送到中央服务器。 中央服务器统计统计数据，并向每个当地医疗中心提出新的统计数据。 在迭代方法中，预测模型是在没有转移患者数据的情况下开发的，但是对于可从多个医疗中心获得的患者数据进行统计。 为了确保该过程的舒适度，所发送的统计数据可以是人类可读的格式。

    AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS
    4.
    发明申请
    AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS 审中-公开
    医疗卫生系统服务代码的自动映射

    公开(公告)号：US20140095205A1

    公开(公告)日：2014-04-03

    申请号：US14037548

    申请日：2013-09-26

    申请人： Faisal Farooq ,  Marc Overhage ,  Glenn Fung ,  Shipeng Yu ,  Bharat R. Rao ,  Balaji Krishnapuram ,  Jan DeHaan

    发明人： Faisal Farooq ,  Marc Overhage ,  Glenn Fung ,  Shipeng Yu ,  Bharat R. Rao ,  Balaji Krishnapuram ,  Jan DeHaan

    IPC分类号： G06F19/00

    CPC分类号： G16H10/60

    摘要： Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.

    摘要翻译： 提供医疗保健中语义的自动映射。 数据集具有不同的语义（例如，在一个系统中用M和F指定的Gender，在另一个系统中用1或2指定的Sex）。 对于语义互操作性，根据数据本身的统计，处理器创建不同医疗保健实体的语义系统之间的语义链接（例如，Gender = Sex和/或1 = F和2 = M）。 使用或不使用其他信息和/或逻辑的变量，值或变量和值的分布用于创建从一个语义系统到另一个语义系统的映射。 其他变量和/或值的类似分布可能是具有相同含义的变量和/或值。

    Computer-Based Patient Management for Healthcare
    6.
    发明申请
    Computer-Based Patient Management for Healthcare 审中-公开
    基于计算机的医疗患者管理

    公开(公告)号：US20120065987A1

    公开(公告)日：2012-03-15

    申请号：US13228497

    申请日：2011-09-09

    申请人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    发明人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    IPC分类号： G06Q50/22

    CPC分类号： G06Q50/24 ,  G06F19/328 ,  G06Q10/0633 ,  G06Q10/10 ,  G06Q10/103 ,  G06Q10/109 ,  G06Q50/22 ,  G16H10/60 ,  G16H40/20 ,  G16H50/70

    摘要： Computer-based patient management is provided for healthcare. Patient data is used to determine a severity, assign a patient to a corresponding diagnosis-related group, and provide a timeline for care at a medical facility. Reminders or alerts are sent to maintain the timeline for more cost effective care. Reminders, suggestions, transitions between care givers, scheduling and other risk management actions are performed. As more data becomes available as part of the care, the care and timeline may be adjusted automatically for more efficient utilization of resources. Accountable care optimization is provided as part of case management. Automated care before any injury or illness and automated care after discharge is provided to optimize the health and costs for a patient. The patient is assigned to the cohort based on the patient data.

    摘要翻译： 为医疗保健提供基于计算机的病人管理。 患者数据用于确定严重程度，将患者分配给相应的诊断相关组，并在医疗机构提供护理时间表。 发送提醒或警报以保持更节省成本的时间表。 执行提醒，建议，护理人员之间的转换，调度和其他风险管理操作。 随着越来越多的数据作为护理的一部分可用，护理和时间表可以自动调整以更有效地利用资源。 作为病例管理的一部分提供了责任护理优化。 任何伤害或疾病之前的自动化护理以及出院后的自动化护理，以优化患者的健康和成本。 根据患者数据将患者分配到队列。

    Healthcare Information Technology System for Predicting and Preventing Adverse Events
    7.
    发明申请
    Healthcare Information Technology System for Predicting and Preventing Adverse Events 审中-公开
    保健信息技术系统预防和预防不良事件

    公开(公告)号：US20110295621A1

    公开(公告)日：2011-12-01

    申请号：US13153526

    申请日：2011-06-06

    申请人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    发明人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    IPC分类号： G06Q50/00 ,  G06N5/04 ,  G06F15/18

    CPC分类号： G06Q50/24 ,  G06Q10/10 ,  G16H10/60 ,  G16H40/20 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70

    摘要： An adverse event may be prevented by predicting the probability of a given patient to have or undergo the adverse event. The probability alone may prevent the adverse event by educating the patient or medical professional. The probability may be predicted at any time, such as upon entry of information for the patient, periodic analysis, or at the time of admission. The probability may be used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding adverse event. The probability may be specific to a hospital, physician group, or other medical entity, allowing prevention to focus on past adverse event causes for the given entity.

    摘要翻译： 可以通过预测给定患者具有或经历不良事件的可能性来防止不良事件。 单靠概率可以通过教育患者或医疗专业人员来预防不良事件。 可以随时预测概率，例如在输入患者信息，定期分析或入院时。 概率可以用于生成工作流动作项目以降低概率，警告输出适当的指令和/或协助避免不利事件。 医院，医师团体或其他医疗机构的概率可能是特定的，允许预防集中于给定实体的过去不良事件原因。

    Automatic Processing of Handwritten Physician Orders
    8.
    发明申请
    Automatic Processing of Handwritten Physician Orders 审中-公开
    自动处理手写医师订单

    公开(公告)号：US20120065997A1

    公开(公告)日：2012-03-15

    申请号：US13228776

    申请日：2011-09-09

    申请人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    发明人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    IPC分类号： G06Q50/22

    CPC分类号： G06Q50/22 ,  G06Q50/24

    摘要： Physician orders are automatically processed. Rather than requiring entry with a user interface in a computerized order entry system, physician orders may be handwritten on a piece of paper or entered on another handwriting device. The orders are scanned or transmitted. Using a lexicon limited to the vocabulary of possible orders, handwriting recognition is applied to the scanned order. By limiting the lexicon, the accuracy of the optical character recognition may be increased. The lexicon may be further limited by determining a diagnosis and/or treatment or imaging modality for the patient and selecting a lexicon limited to orders associated with the diagnosis or modality. The recognized order is then implemented by the computerized order entry system.

    摘要翻译： 医师订单自动处理。 不需要在计算机化订单输入系统中输入用户界面，医生指令可以手写在一张纸上或者输入另一个手写装置。 订单被扫描或传输。 使用限于可能订单的词汇表的词典，手写识别将应用于扫描的订单。 通过限制词汇，可以增加光学字符识别的准确性。 可以通过确定患者的诊断和/或治疗或成像模式并选择限于与诊断或模态相关联的命令的词典来进一步限制词典。 所识别的订单然后由计算机订单输入系统实现。

    Computerized Surveillance of Medical Treatment
    9.
    发明申请
    Computerized Surveillance of Medical Treatment 审中-公开
    电脑化医疗监察

    公开(公告)号：US20120041784A1

    公开(公告)日：2012-02-16

    申请号：US13228661

    申请日：2011-09-09

    申请人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    发明人： Faisal Farooq ,  Romer E. Rosales ,  Shipeng Yu ,  Balaji Krishnapuram ,  Bharat R. Rao

    IPC分类号： G06Q50/24

    CPC分类号： G06Q50/24 ,  G16H10/20 ,  G16H50/70

    摘要： Medical treatment is automatically surveyed. Drugs or other treatments may be monitored post-market. This surveillance may be accomplished in two ways: (1) Identify patients that potentially match templates consistent with possible adverse reactions, possibly including adverse reactions not associated with the treatment. Potentially, if the match is good enough, a single patient may be sufficient to raise an alert. Alternately, multiple patients partially matching a template may cause an alert. (2) Identify patient clusters with unusual patterns. Multiple patients associated with greater rates of adverse events or event severity not expected with the treatment are identified. The data for surveillance is acquired from multiple sources, so may be more comprehensive for early recognition of adverse effects. Data gathering and surveillance are computerized, so early, cost effective recognition may be more likely.

    摘要翻译： 医疗自动进行调查。 药物或其他治疗方法可能在市场后监测。 这种监测可以通过以下两种方式完成：（1）确定潜在匹配模板的患者与可能的不良反应一致，可能包括与治疗无关的不良反应。 潜在地，如果比赛足够好，单个病人可能足以提高警戒。 或者，多个部分匹配模板的患者可能会引起警惕。 （2）识别具有异常模式的患者集群。 确定与治疗无法预期的不良事件或事件严重程度相关的多个患者。 监测数据来自多个来源，因此可能会更加全面地早期识别不良反应。 数据收集和监控是电脑化的，所以早期，成本有效的识别可能更有可能。