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1. 发明申请

WO2017149530A1 SYSTEM AND METHOD FOR SELECTING OPTIMAL MEDICATIONS FOR A SPECIFIC PATIENT 审中-公开
标题翻译：用于为特定患者选择最佳药物的系统和方法
公开(公告)号：WO2017149530A1
公开(公告)日：2017-09-08
申请号：PCT/IL2017/050246
申请日：2017-02-27
申请人： MOR RESEARCH APPLICATIONS LTD
发明人： NETZER, Doron , NABRISKI, Dan , ROITMAN, Eytan
IPC分类号： G06F19/00
CPC分类号： G16H20/10 , G06F19/3456 , G16H10/60 , G16H50/20
摘要： Embodiments disclosed herein relate to a computerized method of selecting an optimal medication for a patient, comprising: providing a user with communication access to a central server having in memory a database of a rule set associated with a list of medications available, the rules set numerically ranking the suitability of a medication for a patient; allowing upload of at least a portion of an electronic medical record for an identified patient; allowing input of a patient diagnosis, and receiving a search request for a suitable treatment medication; comparing the rules set, with the uploaded electronic medical record; determining (e.g., scoring and calculating) the suitability of medications from the list of medications available; providing a displayable list of medications suited for the patient, wherein the list is ranked according to the preference of the medications for the patient.
摘要翻译：本文公开的实施例涉及为患者选择最佳药物的计算机化方法，包括：向用户提供对中央服务器的通信访问，所述中央服务器在存储器中具有与列表相关联的规则集的数据库的可用药物，规则集合在数字上对药物对患者的适用性进行排序; 允许为识别的患者上载至少一部分电子医疗记录; 允许输入患者诊断，并接收对合适的治疗药物的搜索请求; 将规则集与上传的电子病历进行比较; 从可用药物列表中确定（例如评分和计算）药物的适用性; 提供适合于患者的药物的可显示列表，其中列表根据药物对患者的偏好进行排序。

2. 发明申请

WO2022200967A1 MACHINE LEARNING MODELS FOR DESIGNATION OF SUBJECTS FOR TREATMENT AND/OR EVALUATION 审中-公开
公开(公告)号：WO2022200967A1
公开(公告)日：2022-09-29
申请号：PCT/IB2022/052517
申请日：2022-03-20
申请人： MOR RESEARCH APPLICATIONS LTD.
发明人： NETZER, Doron , BALICER, Ran , DAGAN, Noa , KEPTEN, Eldad , LEIBOVICH, Nava , MIRON, Yifat , BLOOMENTHAL, Shlomit , WAXMAN, Jacob Gershon , SAFAR, Ronit , MAHANAIMY, Moria , ROITMAN KALISHOV, Ilana
IPC分类号： G06N20/00 , G16H40/20 , G16H10/60
摘要： A method comprises: for each specific medical intervention and/or respective clinical outcome: accessing a respective specific priority list of a respective sub-set of subjects scheduled in a prioritized sequence for treatment and/or evaluation, creating a respective specific training dataset that includes data extracted from EMRs of the respective sub-set of subjects labelled with the specific priority list, and training a respective specific machine learning model on the respective specific training dataset for generating an outcome of a respective specific priority list of a sub-set of subjects for prioritized evaluation and/or treatment, in response to an input of data extracted from EMR of the sub-set of subjects, accessing a combined prioritization component for generating an outcome of a combined priority list of subjects for prioritized evaluation and/or treatment in response to an input of outcomes of the specific machine learning models, and providing the specific models and the combined prioritization component.

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