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1. 发明申请

US20140088989A1 Rapid Learning Community for Predictive Models of Medical Knowledge 审中-公开
标题翻译：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。

2. 发明申请

US20140095204A1 AUTOMATED MEDICAL COHORT DETERMINATION 审中-公开
标题翻译：自动化医疗协调决定
公开(公告)号：US20140095204A1
公开(公告)日：2014-04-03
申请号：US14037469
申请日：2013-09-26
申请人： Glenn Fung , Balaji Krishnapuram , Faisal Farooq , Shipeng Yu , Bharat R. Rao , Vikram Anand
发明人： Glenn Fung , Balaji Krishnapuram , Faisal Farooq , Shipeng Yu , Bharat R. Rao , Vikram Anand
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G16H50/70
摘要： Inclusion of a patient in a medical category is determined by triggering an analysis of an electronic medical record of the patient in response to an input of data into the electronic medical record. Identifying characteristics that indicate inclusion in the medical category with the analysis, and determining a probability the patient belongs to the medical category based on the identified characteristics.
摘要翻译：通过响应于将数据输入到电子医疗记录中的触发对患者的电子医疗记录的分析来确定医疗类别中的患者的包含。通过分析来识别指示包含在医疗类别中的特征，并且基于所识别的特征来确定患者属于医疗类别的概率。

3. 发明申请

US20140095205A1 AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS 审中-公开
标题翻译：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。

4. 发明申请

US20140095203A1 MEDICAL WORKFLOW DETERMINATION AND OPTIMIZATION 审中-公开
标题翻译：医疗工作流程确定与优化
公开(公告)号：US20140095203A1
公开(公告)日：2014-04-03
申请号：US14035989
申请日：2013-09-25
申请人： Vikram Anand , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Faisal Farooq , Jonathan D. Emanuele
发明人： Vikram Anand , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Faisal Farooq , Jonathan D. Emanuele
IPC分类号： G06Q50/24 , G06Q10/06
CPC分类号： G16H40/20 , G06F19/325 , G06Q10/0633 , G06Q50/22 , G06Q50/24 , G16H10/60 , G16H50/50
摘要： Workflows for medical entities are determined and evaluated by determining a plurality of medical tasks based on an analysis of a plurality of electronic medical records of a medical entity. A workflow of the medical entity is determined based on a sequence of medical tasks, the sequence determined based on the analysis of the plurality of electronic medical records, and an evaluation of the workflow is performed based on a predefined criterion.
摘要翻译：通过基于对医疗实体的多个电子病历的分析来确定多个医疗任务来确定和评估医疗实体的工作流程。基于医疗任务的顺序确定医疗实体的工作流程，该顺序是基于多个电子医疗记录的分析确定的顺序，并且基于预定义的标准来执行工作流程的评估。

5. 发明申请

US20140095206A1 ADAPTIVE MEDICAL DOCUMENTATION SYSTEM 审中-公开
标题翻译：自适应医疗文献系统
公开(公告)号：US20140095206A1
公开(公告)日：2014-04-03
申请号：US14039125
申请日：2013-09-27
申请人： Glenn Fung , Balaji Krishnapuram , Faisal Farooq , Shipeng Yu , Mark Overhange , John Haley , Jan DeHaan , Vikram Anand
发明人： Glenn Fung , Balaji Krishnapuram , Faisal Farooq , Shipeng Yu , Mark Overhange , John Haley , Jan DeHaan , Vikram Anand
IPC分类号： G06F19/00
CPC分类号： G16H50/30 , G16H10/60 , G16H50/20
摘要： Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.
摘要翻译：用于医疗实体的适应性医疗数据收集可以涉及触发对输入到患者的电子病历（EMR）的信息的电子记录的分析。根据分析确定患者的潜在病情。识别与患者的潜在状况相关的附加信息，并产生对所识别的附加信息的请求。

6. 发明申请

US20120065987A1 Computer-Based Patient Management for Healthcare 审中-公开
标题翻译：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。

7. 发明授权

US08949082B2 Healthcare information technology system for predicting or preventing readmissions 有权
标题翻译：用于预防或预防再入院的医疗信息技术系统
公开(公告)号：US08949082B2
公开(公告)日：2015-02-03
申请号：US13153551
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： G06F7/60 , G06F17/10 , G06F19/00 , G06Q10/10 , G06Q50/24
CPC分类号： G06F19/3431 , G06F19/00 , G06Q10/10 , G06Q50/24 , G16H10/60 , G16H40/20 , G16H50/30 , G16H50/70
摘要： Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.
摘要翻译：可能会阻止医院再入院。通过预测给定患者被重新接受的概率来防止再入院。单靠概率可以通过教育病人或医疗专业人员来预防再入院。可以在患者停留期间预测概率，并且用于产生工作流动作项目以降低概率，警告输出适当指令和/或协助避免再次入院。医院，医师团体或其他实体的概率可能是特定的，允许预防专注于给定实体的过去重新接受原因。

8. 发明申请

US20110295621A1 Healthcare Information Technology System for Predicting and Preventing Adverse Events 审中-公开
标题翻译：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。

9. 发明申请

US20120065997A1 Automatic Processing of Handwritten Physician Orders 审中-公开
标题翻译：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。

10. 发明申请

US20120041784A1 Computerized Surveillance of Medical Treatment 审中-公开
标题翻译：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。

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