机构：[1]Institute of Image Communication and Networking, Shanghai Jiao Tong University, Shanghai, China[2]Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
Studies of the genotype-phenotype associations for diseases such as type 2 diabetes mellitus (T2DM) become increasingly popular in recent years. Commonly used methods are genome-wide association study (GWAS) and phenome-wide association study (PheWAS). To perform the above analysis, it is necessary to identify T2DM subjects' cases and controls based on electronic health records (EHR). However, the existing expert-based identification algorithms often have a low recall and miss a large number of the valuable samples under conservative filtering standards. As a pilot study, this paper proposed a semi-automated framework based on machine learning. We target to optimize the filtering criteria to improve recall at the same time keeping low false-positive rate. We validate the proposed framework using a EHR database with ten years of records and show the effectiveness of the proposed framework.
语种：
外文
被引次数：
WOS：
第一作者：
第一作者机构：[1]Institute of Image Communication and Networking, Shanghai Jiao Tong University, Shanghai, China[2]Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
通讯作者：
通讯机构：[1]Institute of Image Communication and Networking, Shanghai Jiao Tong University, Shanghai, China[2]Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
推荐引用方式(GB/T 7714)：
Zheng Tao,Zhang Ya.A Big Data Application of Machine Learning-Based Framework to Identify Type 2 Diabetes Through Electronic Health Records[J].KNOWLEDGE MANAGEMENT IN ORGANIZATIONS (KMO 2017).2017,731:451-458.doi:10.1007/978-3-319-62698-7_37.
APA：
Zheng, Tao&Zhang, Ya.(2017).A Big Data Application of Machine Learning-Based Framework to Identify Type 2 Diabetes Through Electronic Health Records.KNOWLEDGE MANAGEMENT IN ORGANIZATIONS (KMO 2017),731,
MLA：
Zheng, Tao,et al."A Big Data Application of Machine Learning-Based Framework to Identify Type 2 Diabetes Through Electronic Health Records".KNOWLEDGE MANAGEMENT IN ORGANIZATIONS (KMO 2017) 731.(2017):451-458
