Free-Text Disease Classification

Abstract

Modern medicine produces data with every patient interaction. While many data elements are easily captured and analyzed, the fundamental record of the patient/clinician interaction is captured in written, free-text. This thesis provides the foundation for the Military Health System to begin building an auto classifier for ICD9 diagnostic codes based on free-text clinician notes. Support Vector Machine models are fit to approximately 84,000 free-text records providing a means to predict ICD9 codes for other free-text records. While the research conducted in this thesis does not provide a consumate ICD9 classification model, it does provide the foundation required to further more detailed analysis.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2011
Accession Number
ADA551980

Entities

People

  • Craig Maxey

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Business Administration
  • Computer Languages
  • Data Mining
  • Data Processing
  • Data Sets
  • Delivery Of Health Care
  • Department Of Defense
  • Diseases And Disorders
  • Health Care
  • Health Services
  • Information Processing
  • Information Science
  • Machine Learning
  • Medical Personnel
  • Supervised Machine Learning
  • United States
  • Xml

Readers

  • Database Systems and Applications
  • Mental Health of Military Veterans with Posttraumatic Stress Disorder (PTSD): Risk Factors, Prevalence, Symptoms, and Treatment.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval