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1.发明授权Mining temporal patterns in longitudinal event data using discrete event matrices and sparse coding 有权使用离散事件矩阵和稀疏编码挖掘纵向事件数据中的时间模式公开(公告)号：US08583586B2
公开(公告)日：2013-11-12
申请号：US13011632
申请日：2011-01-21
申请人： Shahram Ebadollahi , Jianying Hu , Martin S. Kohn , Noah Lee , Robert K. Sorrentino , Jimeng Sun , Fei Wang
发明人： Shahram Ebadollahi , Jianying Hu , Martin S. Kohn , Noah Lee , Robert K. Sorrentino , Jimeng Sun , Fei Wang
IPC分类号： G06N5/02
CPC分类号： G06N99/005
摘要： Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.
摘要翻译： 示出了用于事件模式挖掘的方法和系统，其包括将可测量几何空间中的纵向事件数据表示为使用空间时间形状的时间事件矩阵表示（TEMR），其中事件数据被组织成事件类型的分级类别并执行时间事件模式 通过使用约束稀疏编码框架在所述TEMR的空间时间形状之间定位视觉事件模式来利用处理器进行挖掘。
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2.发明申请METHOD, SYSTEM AND COMPUTER PROGRAM PRODUCT FOR AGGREGATING POPULATION DATA 审中-公开用于聚集人口数据的方法，系统和计算机程序产品公开(公告)号：US20130231953A1
公开(公告)日：2013-09-05
申请号：US13409890
申请日：2012-03-01
CPC分类号： G06Q50/22
摘要： A system, method and program product for matching members of a population, e.g., patients, based on member similarities. Patients are mapped to a bipartite graph with patient nodes connected by weighted edges to clustered factor nodes, are clustered categorically. As a new patient query is received, a similarity measure for each other patient is generated for each cluster by comparing cluster edges. The cluster similarity measures are aggregated for each patient to provide a global closeness measure to every other patient. Based on the global closeness measure, a list of the closest patients is displayed and measurement feedback may be provided.
摘要翻译： 一种系统，方法和程序产品，用于根据成员的相似性来匹配人群，例如患者的成员。 病人被映射到一个二分图，患者节点通过加权边缘连接到聚类因子节点，被明确地聚类。 当接收到新的患者查询时，通过比较群集边缘，为每个群集生成每个其他患者的相似性度量。 针对每个患者聚集群集相似性度量，以向每个其他患者提供全球接近度量度。 根据全球接近度测量，显示最接近患者的列表，并可提供测量反馈。
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3.发明申请MINING TEMPORAL PATTERNS IN LONGITUDINAL EVENT DATA USING DISCRETE EVENT MATRICES AND SPARSE CODING 有权使用离散事件矩阵和稀疏编码的长期事件数据挖掘时间模式公开(公告)号：US20120191640A1
公开(公告)日：2012-07-26
申请号：US13011632
申请日：2011-01-21
申请人： Shahram Ebadollahi , Jiaying Hu , Martin S. Kohn , Noah Lee , Robert K. Sorrentino , Jimeng Sun , Fei Wang
发明人： Shahram Ebadollahi , Jiaying Hu , Martin S. Kohn , Noah Lee , Robert K. Sorrentino , Jimeng Sun , Fei Wang
IPC分类号： G06N5/02
CPC分类号： G06N99/005
摘要： Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.
摘要翻译： 示出了用于事件模式挖掘的方法和系统，其包括将可测量几何空间中的纵向事件数据表示为使用空间时间形状的时间事件矩阵表示（TEMR），其中事件数据被组织成事件类型的分级类别并执行时间事件模式 通过使用约束稀疏编码框架在所述TEMR的空间时间形状之间定位视觉事件模式来利用处理器进行挖掘。
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4.发明申请METHOD AND SYSTEM FOR OUTCOME BASED REFERRAL USING HEALTHCARE DATA OF PATIENT AND PHYSICIAN POPULATIONS 有权使用患者和医疗人群健康数据的基于结果的参考方法和系统公开(公告)号：US20120109683A1
公开(公告)日：2012-05-03
申请号：US12913042
申请日：2010-10-27
申请人： SHAHRAM EBADOLLAHI , Jianying Hu , Martin S. Kohn , Jonathan D. Laserson , Hani Neuvirth-Telem , Lavi Shpigelman , Robert K. Sorrentino
发明人： SHAHRAM EBADOLLAHI , Jianying Hu , Martin S. Kohn , Jonathan D. Laserson , Hani Neuvirth-Telem , Lavi Shpigelman , Robert K. Sorrentino
IPC分类号： G06Q50/00
CPC分类号： G06Q50/24 , G06F19/00 , G06Q10/06393
摘要： A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.
摘要翻译： 推荐系统和方法包括提取当前患者的患者特征以产生当前患者的表示。 将当前患者的患者特征与来自医学相关记录的一组患者的一个或多个医生的医师特征和患者对医生特征进行比较。 与医生相关的结果指标与当前查询进行比较。 基于从功能和结果测量输出构建的至少一个预测模型，对患者，医师对的未来结果进行预测。
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公开(公告)号：US20120041772A1
公开(公告)日：2012-02-16
申请号：US12855060
申请日：2010-08-12
IPC分类号： G06Q50/00
摘要： A system and method for predicting patient prognosis includes a similarity module configured in program storage media to provide a similarity function for a data source and compute similarity scores for pairs of patients. An alignment module is configured to align a query patient to a best anchor timestamp of a similar patient or patients so that a comparison between the query patient and at least one similar patient is provided. A prediction module is configured to predict a long-term outcome measure of the query patient based on data from the at least one similar patient.
摘要翻译： 用于预测患者预后的系统和方法包括在程序存储介质中配置的相似性模块，以为数据源提供相似性功能，并计算患者对的相似性评分。 配对模块被配置成将查询患者与相似患者或患者的最佳锚定时间戳对齐，以便提供查询患者与至少一个类似患者之间的比较。 预测模块被配置为基于来自所述至少一个相似患者的数据来预测所述查询患者的长期结果测量。
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公开(公告)号：US08620690B2
公开(公告)日：2013-12-31
申请号：US13312685
申请日：2011-12-06
CPC分类号： G06Q50/22 , G06F19/00 , G06Q10/06398 , G16H40/20 , G16H50/30
摘要： A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations are solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.
摘要翻译： 获得多个实际结果数据点，包括多个过程发作的实际结果。 也为该过程获得了与从业者无关的基线结果。 对于每个给定的实际结果数据点，给定的实际结果数据点之一等于与从业者实体无关的基线结果乘以多个参与从业者实体结果指数，用于多个参与实践者实体中的每一个 。 每个参与的从业者实体结果指数被提升到包括多个未知参与从业者实体类型索引中的相应一个的指数，以获得多个等式。 解决多个等式以获得未知参与实践者实体结果指数的估计值和未知参与实践者实体类型索引的估计值。
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7.发明授权Method and system for outcome based referral using healthcare data of patient and physician populations 有权使用患者和医师人群的医疗保健数据进行结果转诊的方法和系统公开(公告)号：US08660857B2
公开(公告)日：2014-02-25
申请号：US12913042
申请日：2010-10-27
申请人： Shahram Ebadollahi , Jianying Hu , Martin S. Kohn , Jonathan D. Laserson , Hani Neuvirth-Telem , Lavi Shpigelman , Robert K. Sorrentino
发明人： Shahram Ebadollahi , Jianying Hu , Martin S. Kohn , Jonathan D. Laserson , Hani Neuvirth-Telem , Lavi Shpigelman , Robert K. Sorrentino
CPC分类号： G06Q50/24 , G06F19/00 , G06Q10/06393
摘要： A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.
摘要翻译： 推荐系统和方法包括提取当前患者的患者特征以产生当前患者的表示。 将当前患者的患者特征与来自医学相关记录的一组患者的一个或多个医师的医师特征和患者对医师特征进行比较。 与医生相关的结果指标与当前查询进行比较。 基于从功能和结果测量输出构建的至少一个预测模型，对患者，医师对的未来结果进行预测。
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公开(公告)号：US20130144639A1
公开(公告)日：2013-06-06
申请号：US13312685
申请日：2011-12-06
IPC分类号： G06Q50/22
CPC分类号： G06Q50/22 , G06F19/00 , G06Q10/06398 , G16H40/20 , G16H50/30
摘要： A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations arc solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.
摘要翻译： 获得多个实际结果数据点，包括多个过程发作的实际结果。 也为该过程获得了与从业者无关的基线结果。 对于每个给定的实际结果数据点，给定的实际结果数据点之一等于与从业者实体无关的基线结果乘以多个参与从业者实体结果指数，用于多个参与实践者实体中的每一个 。 每个参与的从业者实体结果指数被提升到包括多个未知参与从业者实体类型索引中的相应一个的指数，以获得多个等式。 解决了多个方程，以获得未知参与实践者实体结果指数和未知参与实践者实体类型指数的估计值的估计值。
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9.发明申请DETECTING UNEXPECTED HEALTHCARE UTILIZATION BY CONSTRUCTING CLINICAL MODELS OF DOMINANT UTILIZATION GROUPS 审中-公开通过构建主要利用组的临床模型来检测意外健康利用公开(公告)号：US20120209620A1
公开(公告)日：2012-08-16
申请号：US13028753
申请日：2011-02-16
IPC分类号： G06Q50/00
CPC分类号： G06Q10/06393 , G06Q10/0637 , G06Q50/22
摘要： A system and method for identifying unexpected utilization profiles at a patient level includes determining one or more clusters that have a profile based on patient profiles and building a representative model for each cluster including demographic and clinical information. Using the model, demographic and clinical characteristics are determined which form expected utilization cluster. An expected utilization cluster for each patient, which is derived from the demographic features and the clinical characteristics, is compared against an actual utilization profile for that patient to determine whether the actual utilization profile is unexpected.
摘要翻译： 用于识别患者级别的意外利用简档的系统和方法包括基于患者简档确定具有简档的一个或多个聚类，并为包括人口统计学和临床信息的每个聚类构建代表性模型。 使用模型，确定形成预期利用集群的人口统计学和临床特征。 将根据人口统计特征和临床特征导出的每个患者的预期利用集群与该患者的实际利用率曲线进行比较，以确定实际利用率曲线是否是意外的。
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10.发明申请公开(公告)号：US20120041277A1
公开(公告)日：2012-02-16
申请号：US12855068
申请日：2010-08-12
IPC分类号： A61B5/00
CPC分类号： A61B5/0205 , A61B5/02055 , A61B5/021 , A61B5/024 , A61B5/0402 , A61B5/14542 , A61B5/7257 , A61B5/726 , G16H50/70
摘要： A system and method for predicting near term measurements of a patient includes a stream processor configured to summarize raw measurements from patients into signatures and construct optimal prediction models based on previously obtained signatures. A similar patient tracker is configured to monitor similar patient information for a query patient. The similar patient information is determined based on a similarity between the query patient and signatures of other patients. A model analyzer is configured to employ retrofitted optimal prediction models from similar patients to predict near term measurements of the query patient.
摘要翻译： 用于预测患者的近期测量的系统和方法包括流处理器，其被配置为将来自患者的原始测量总结为签名，并且基于先前获得的签名构建最佳预测模型。 类似的患者跟踪器被配置为监视查询患者的类似患者信息。 基于查询患者和其他患者的签名之间的相似性来确定类似的患者信息。 模型分析器被配置为使用来自类似患者的改进的最佳预测模型来预测查询患者的近期测量。
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