Analysis of Primary Care Computerised Medical Records with Deep Learning

被引：1

作者：

de Lusignan, Simon ^{[1
,3
]}

Smith, Nadia ^{[2
]}

Livina, Valerie ^{[2
]}

Yonova, Ivelina ^{[1
,3
]}

Webb, Rebecca ^{[3
]}

Thomas, Spencer A. ^{[2
]}

机构：

[1] Univ Surrey, Dept Clin & Expt Med, Guildford, Surrey, England

[2] Natl Phys Lab, Hampton Rd, Teddington TW11 0LW, Middx, England

[3] Royal Coll Gen Practitioners, London, England

来源：

ICT FOR HEALTH SCIENCE RESEARCH | 2019年 / 258卷

关键词：

Episode type; general practice; computerized medical record; deep learning; visualization;

D O I：

10.3233/978-1-61499-959-1-249

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The analysis of primary care data plays an important role in understanding health at an individual and population level. Currently the utilization of computerized medical records is low due to the complexities, heterogeneities and veracity associated with these data. We present a deep learning methodology that clusters 11,000 records in an unsupervised manner identifying non-linear patterns in the data. This provides a useful tool for visualization as well as identify features driving the formation of clusters. Further analysis reveal the features that differentiate sub-groups that can aid clinical decision making. Our results uncover subsets that contain the highest proportion of missing data, specifically Episode type, as well as the sources that provide the most complete data.

引用

页码：249 / 250

页数：2

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