Identifying Patients with Coronary Microvascular Dysfunction using Machine Learning

被引：0

作者：

Fodeh, Samah ^{[1
]}

Li, Taihua ^{[2
]}

Jarad, Haya ^{[3
]}

Safdar, Basmah ^{[4
]}

机构：

[1] Yale Univ, Yale Ctr Med Informat, New Haven, CT 06520 USA

[2] Depaul Univ, Coll Comp & Digital Media, Chicago, IL 60604 USA

[3] Univ Connecticut, Sch Engn, Storrs, CT USA

[4] Yale Univ, Dept Emergency Med, New Haven, CT 06520 USA

来源：

2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW) | 2018年

关键词：

Bioinformatics; natural Language Processing; Coronary; Artery; Microvascular; Dysfunction; Classification; Clinical Notes; Machine Learning; CHEST-PAIN; ARTERY-DISEASE; CLASSIFICATION; ARCHITECTURE; ISCHEMIA; ANGINA; WOMEN;

D O I：

10.1109/ICDMW.2018.00109

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 national Research Consensus Conference on Gender-Specific Research in Emergency Care. In this study, we utilized methods from machine learning that leverage structured and unstructured narratives in clinical notes to detect patients with CMD. We have shown that structured data are not sufficient to detect CMD and integrating unstructured data in the computational model boosts the performance significantly.

引用

页码：715 / 721

页数：7

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