Mining Sequential Risk Patterns from Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease

Yi Ting Cheng, Yu Feng Lin, Kuo Hwa Chiang, Vincent Shin-Mu Tseng

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Chronic diseases have been among the major concerns in medical fields since they may cause a heavy burden on healthcare resources and disturb the quality of life. In this paper, we propose a novel framework for early assessment on chronic diseases by mining sequential risk patterns with time interval information from diagnostic clinical records using sequential rules mining, and classification modeling techniques. With a complete workflow, the proposed framework consists of four phases namely data preprocessing, risk pattern mining, classification modeling, and post analysis. For empiricasl evaluation, we demonstrate the effectiveness of our proposed framework with a case study on early assessment of COPD. Through experimental evaluation on a large-scale nationwide clinical database in Taiwan, our approach can not only derive rich sequential risk patterns but also extract novel patterns with valuable insights for further medical investigation such as discovering novel markers and better treatments. To the best of our knowledge, this is the first work addressing the issue of mining sequential risk patterns with time-intervals as well as classification models for early assessment of chronic diseases.

Original languageEnglish
Article number7833052
Pages (from-to)303-311
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume21
Issue number2
DOIs
StatePublished - 1 Mar 2017

Keywords

  • Data mining
  • Sequential patterns
  • disease risk assessment
  • early prediction
  • electronic medical records

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