Development and Validation of Various Phenotyping Algorithms for Diabetes Mellitus Using Data from Electronic Health Records
Precision medicine requires extremely large samples. Electronic health records (EHR) are thought to be a costeffective source of data for that purpose. Phenotyping algorithms help reduce classification errors, making EHR a more reliable source of information for research. Four algorithm development strategies for classifying patients according to their diabetes status (diabetics; non-diabetics; inconclusive) were tested (one codes-only algorithm; one boolean algorithm, four statistical learning algorithms and six stacked generalization meta-learners). The best performing algorithms within each strategy were tested on the validation set. The stacked generalization algorithm yielded the highest Kappa coefficient value in the validation set (0.95 95% CI 0.91, 0.98). The implementation of these algorithms allows for the exploitation of data from thousands of patients accurately, greatly reducing the costs of contructing retrospective cohorts for research.
Diabetes Mellitus Algorithms Precision Medicine
Santiago Esteban Manuel Rodríguez Tablado Francisco Peper Yamila S.Mahumud Ricardo I.Ricci Karin Kopitowski Sergio Terrasa
Family and Community Medicine Division,Hospital Italiano,Buenos Aires,Argentina
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
366-369
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）