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公开(公告)号：US20160292372A1
公开(公告)日：2016-10-06
申请号：US14442517
申请日：2013-11-15
CPC分类号： G16H50/20 , G06N3/0472 , G06N3/08
摘要： A method of identifying an optimum treatment for a patient suffering from coronary artery disease, comprising: (i) providing patient information selected from: (a) status in the patient of one or more coronary disease associated biomarkers; (b) one or more items of medical history information selected from prior condition history, intervention history and medication history; (c) one or more items of diagnostic history, if the patient has a diagnostic history; and (d) one or more items of demographic data; (ii) aggregating the patient information in: (a) a Bayesian network; (b) a machine learning and neural network; (c) a rule-based system; and (d) a regression-based system; (iii) deriving a predicted probabilistic adverse event outcome for each intervention comprising percutaneous coronary intervention by placement of a bare metal stent, or a drug-coated stent; or by coronary artery bypass grafting; and (iv) determining the intervention having the lowest predicted probabilistic adverse outcome.
摘要翻译： 一种鉴定患有冠状动脉疾病的患者的最佳治疗的方法，包括：（i）提供患者信息，所述患者信息选自：（a）患者中一种或多种冠状动脉疾病相关生物标志物的状态; （b）从先前情况史，干预史和用药史选择的一项或多项病史信息; （c）患者具有诊断史的一项或多项诊断史; 和（d）一个或多个人口统计数据项目; （ii）在以下方面聚合患者信息：（a）贝叶斯网络; （b）机器学习和神经网络; （c）基于规则的制度; 和（d）基于回归的系统; （iii）通过放置裸金属支架或药物涂层的支架，导出包括经皮冠状动脉介入的每个干预的预测概率不良事件结果; 或通过冠状动脉旁路移植术; 和（iv）确定具有最低预测概率不良结果的干预。
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公开(公告)号：US11450431B2
公开(公告)日：2022-09-20
申请号：US14442517
申请日：2013-11-15
摘要： A method of identifying an optimum treatment for a patient suffering from coronary artery disease, comprising: (i) providing patient information selected from: (a) status in the patient of one or more coronary disease associated biomarkers; (b) one or more items of medical history information selected from prior condition history, intervention history and medication history; (c) one or more items of diagnostic history, if the patient has a diagnostic history; and (d) one or more items of demographic data; (ii) aggregating the patient information in: (a) a Bayesian network; (b) a machine learning and neural network; (c) a rule-based system; and (d) a regression-based system; (iii) deriving a predicted probabilistic adverse event outcome for each intervention comprising percutaneous coronary intervention by placement of a bare metal stent, or a drug-coated stent; or by coronary artery bypass grafting; and (iv) determining the intervention having the lowest predicted probabilistic adverse outcome.
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