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1.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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2.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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3.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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4.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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公开(公告)号：US08700425B2
公开(公告)日：2014-04-15
申请号：US12795106
申请日：2010-06-07
摘要： Current patients in an emergency department of a hospital are described according to their quantity, their triage classification levels, and their wait times to calculate a current patient backlog. A sum of weight-adjusted triage classification levels of all of the current patients is calculated. Current patient arrival rates in the emergency department are tracked to calculate a current change in patient arrival rates, which are compared with historical changes in patient arrival rates. A size of an imminent influx of arriving patients into the emergency department is then predicted based on the current patient backlog, the sum of weight-adjusted triage classification levels of patients currently in the emergency department, and the current change in patient arrival rates.
摘要翻译： 根据医院急诊科目前的患者情况，根据其数量，分类等级和等待时间来描述当前患者积压情况。 计算所有当前患者的体重调整分类水平的总和。 跟踪目前紧急部门的病人到达率，以计算患者到达率的当前变化，与患者到达率的历史变化进行比较。 然后根据目前的患者积压量，目前在急诊部门的患者的重量调整的分诊水平的总和以及患者到达率的当前变化，预测到达患者即将涌入急诊部门的大小。
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公开(公告)号：US08352285B2
公开(公告)日：2013-01-08
申请号：US12797888
申请日：2010-06-10
IPC分类号： G06Q10/00
摘要： An initial triage level classification for a latest patient to arrive at an emergency department (ED) is received. Availability levels of resources needed to treat the latest patient are electronically collected, along with triage level classifications for all other patients currently in the ED. The initial triage level classification of the latest patient is adjusted upward or downward based on the availability levels of resources needed to treat the latest patient and based on the triage level classifications for the patients in the ED. The triage level classifications for all patients currently in the ED are summed up. If a sum of all triage level classifications exceeds a first predetermined threshold, other resources are reallocated in order to provide the resources needed to treat the latest patient to arrive at the ED. If the sum of all triage level classifications exceeds a second predetermined threshold, then a disaster plan is implemented.
摘要翻译： 收到最近一名患者到达急诊科（ED）的初步分级水平分类。 电子收集治疗最新患者所需资源的可用性水平，以及目前在ED中所有其他患者的分类水平分类。 根据治疗最新患者所需资源的可用性水平，并根据ED患者的分类水平分类，最新患者的初步分级水平分类向上或向下调整。 总结了目前在ED中所有患者的分类水平分类。 如果所有分级等级分类的总和超过第一预定阈值，则重新分配其他资源，以便提供治疗最新患者到达ED所需的资源。 如果所有分级等级分类的总和超过第二预定阈值，则实施灾难计划。
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