A Method to Predict Diagnostic Codes for Chronic Diseases using Machine Learning Techniques

被引：0

作者：

Gupta, Deepa ^{[1
]}

Khare, Sangita ^{[1
]}

Aggarwal, Ashish ^{[2
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Bangalore, Karnataka, India

[2] Innowhirl LLC, San Diego, CA USA

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA) | 2016年

关键词：

CMS data; ICD9; codes; Machine Learning; Chronic Diseases; InfoGain; Adaboost;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Healthcare in simplest form is all about diagnosis and prevention of disease or treatment of any injury by a medical practitioner. It plays an important role in providing quality life for the society. The concern is how to provide better service with less expensive therapeutically equivalent alternatives. Machine Learning techniques (ML) help in achieving this goal. Healthcare has various categories of data like clinical data, claims data, drugs data and hospital data. This paper focuses on clinical and claims data for studying 11 chronic diseases such as kidney disease, osteoporosis, arthritis etc. using the claims data. The correlation between the chronic diseases and the corresponding diagnostic tests is analyzed, by using ML techniques. An effective conclusion on various diagnostics for each chronic disease is made, keeping in mind the clinical relevance.

引用

页码：281 / 287

页数：7

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