Free-Text Disease Classification
NAVAL POSTGRADUATE SCHOOL MONTEREY CA
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Modern medicine produces data with every patient interaction. While many data elements are easily captured and analyzed, the fundamental record of the patientclinician 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.
- Information Science
- Medicine and Medical Research
- Statistics and Probability