公开(公告)号：US08996428B2

公开(公告)日：2015-03-31

申请号：US13351243

申请日：2012-01-17

申请人： Dorit Baras ,  Ariel Farkash ,  Edward Vitkin

发明人： Dorit Baras ,  Ariel Farkash ,  Edward Vitkin

IPC分类号： G06F15/18 ,  G06F19/00

CPC分类号： G06F19/3437 ,  G06F19/00 ,  G16H50/20 ,  G16H50/50

摘要： Method, system, and computer program product are provided for predicting diagnosis of a patient performed by a computerized device. The method may include: modeling data from a group of successfully diagnosed patients, wherein the data is modeled as treatment paths of patients including referrals to medical practitioners; and predicting diagnosis for a current patient by comparing a treatment path of the current patient with the modeled treatment paths of successfully diagnosed patients, including calculating a probability of a given diagnosis from the modeled treatment paths. The method may include: defining a set of medical entities including medical practitioners to which a patient has been referred; and gathering treatment paths of successfully diagnosed patients, wherein the treatment path links medical entities in a directional route. Predicting diagnosis for a current patient may use the modeled data to calculate the probability of each model instance for each diagnosis and choosing the model instance of the diagnosis that maximizes the treatment path probability.

摘要翻译： 提供方法，系统和计算机程序产品，用于预测由计算机化设备执行的患者的诊断。 该方法可以包括：来自一组成功诊断的患者的数据建模，其中数据被建模为患者的治疗路径，包括转诊到医生; 以及通过将当前患者的治疗路径与成功诊断的患者的建模治疗路径进行比较来预测当前患者的诊断，包括从建模的治疗路径计算给定诊断的概率。 该方法可以包括：定义一组医疗实体，包括已经被引用患者的医疗从业者; 并且收集成功诊断的患者的治疗路径，其中所述治疗路径将医疗实体连接在定向路径中。 对当前患者的预测诊断可以使用建模数据来计算每个诊断的每个模型实例的概率，并且选择最大化治疗路径概率的诊断的模型实例。

公开(公告)号：US20130185231A1

公开(公告)日：2013-07-18

申请号：US13351243

申请日：2012-01-17

申请人： Dorit Baras ,  Ariel Farkash ,  Edward Vitkin

发明人： Dorit Baras ,  Ariel Farkash ,  Edward Vitkin

IPC分类号： G06N5/04 ,  G06F15/18

CPC分类号： G06F19/3437 ,  G06F19/00 ,  G16H50/20 ,  G16H50/50

摘要： Method, system, and computer program product are provided for predicting diagnosis of a patient performed by a computerized device. The method may include: modeling data from a group of successfully diagnosed patients, wherein the data is modeled as treatment paths of patients including referrals to medical practitioners; and predicting diagnosis for a current patient by comparing a treatment path of the current patient with the modeled treatment paths of successfully diagnosed patients, including calculating a probability of a given diagnosis from the modeled treatment paths. The method may include: defining a set of medical entities including medical practitioners to which a patient has been referred; and gathering treatment paths of successfully diagnosed patients, wherein the treatment path links medical entities in a directional route. Predicting diagnosis for a current patient may use the modeled data to calculate the probability of each model instance for each diagnosis and choosing the model instance of the diagnosis that maximizes the treatment path probability.

摘要翻译： 提供方法，系统和计算机程序产品，用于预测由计算机化设备执行的患者的诊断。 该方法可以包括：来自一组成功诊断的患者的数据建模，其中数据被建模为患者的治疗路径，包括转诊到医生; 以及通过将当前患者的治疗路径与成功诊断的患者的建模治疗路径进行比较来预测当前患者的诊断，包括从建模的治疗路径计算给定诊断的概率。 该方法可以包括：定义一组医疗实体，包括已经被引用患者的医疗从业者; 并且收集成功诊断的患者的治疗路径，其中所述治疗路径将医疗实体连接在定向路径中。 对当前患者的预测诊断可以使用建模数据来计算每个诊断的每个模型实例的概率，并且选择最大化治疗路径概率的诊断的模型实例。

公开(公告)号：US20120185416A1

公开(公告)日：2012-07-19

申请号：US13007684

申请日：2011-01-17

申请人： Dorit Baras ,  Boaz Carmeli ,  Ohad Greenshpan ,  Edward Vitkin

发明人： Dorit Baras ,  Boaz Carmeli ,  Ohad Greenshpan ,  Edward Vitkin

IPC分类号： G06F15/18

CPC分类号： G06Q10/04 ,  G06F9/5083 ,  G06N3/02 ,  G06Q10/0631 ,  G16H40/20

摘要： Method, system, and computer program product for load estimation in a user-based environment. The method includes: inputting a set of time-dependent, raw operational indicators of the environment; creating a load function according to the specific needs of the environment; displaying an estimated load; receiving user feedback on the estimated load; and applying a dynamic learning mechanism to generated a user-tuned load function for estimating load on the environment. The dynamic learning mechanism may be an informative mechanism that supports backtracking to solve user-adaptability problems.

摘要翻译： 用于基于用户环境的负载估计的方法，系统和计算机程序产品。 该方法包括：输入一组时间依赖的原始操作指标; 根据环境的具体需要创建负载功能; 显示估计负载; 接收用户对估计负载的反馈; 以及应用动态学习机制以产生用于估计环境负荷的用户调整负载函数。 动态学习机制可能是支持回溯以解决用户适应性问题的信息机制。