Danger Roxana, Corrigan Derek, Soler Jean K, Kazienko Przemyslaw, Kajdanowicz Tomasz, Majeed Azeem, Curcin Vasa
Imperial College London, London, UK.
Royal College of Surgeons in Ireland, Dublin, Ireland.
Stud Health Technol Inform. 2015;210:85-9.
Data mining of electronic health records (eHRs) allows us to identify patterns of patient data that characterize diseases and their progress and learn best practices for treatment and diagnosis. Clinical Prediction Rules (CPRs) are a form of clinical evidence that quantifies the contribution of different clinical data to a particular clinical outcome and help clinicians to decide the diagnosis, prognosis or therapeutic conduct for any given patient. The TRANSFoRm diagnostic support system (DSS) is based on the construction of an ontological repository of CPRs for diagnosis prediction in which clinical evidence is expressed using a unified vocabulary. This paper explains the proposed methodology for constructing this CPR repository, addressing algorithms and quality measures for filtering relevant rules. Some preliminary application results are also presented.
电子健康记录（eHRs）的数据挖掘使我们能够识别表征疾病及其进展的患者数据模式，并学习治疗和诊断的最佳实践。临床预测规则（CPRs）是一种临床证据形式，它量化了不同临床数据对特定临床结果的贡献，并帮助临床医生为任何给定患者决定诊断、预后或治疗行为。TRANSFoRm诊断支持系统（DSS）基于构建用于诊断预测的CPRs本体知识库，其中临床证据使用统一词汇表来表达。本文解释了构建此CPR知识库的提议方法，讨论了用于筛选相关规则的算法和质量度量。还展示了一些初步应用结果。
