Modeling document classification to automate mental health diagnosis

<p>The objective of this study is to determine if diagnosis documents can be used with document classification to automatically diagnose mental health conditions. Document classification allows text documents to be analyzed and organized into their appropriate classes based on the features and words presented in the text. One application of this is within the medical field to automatically classify different patient diagnosis based on medical or patient notes. This research applied mental health diagnosis documents to automatically diagnose a group of patients with a mental health condition based on text-based survey data. This classification was approached through several feature engineering and machine learning models to determine the optimal methods for diagnosis classification. A model was created that successfully classified diagnosis documents to their appropriate mental health condition, but due to limitation in the patient dataset, no model successfully classified patient diagnoses.</p>

Name thesis:degree_name

Master of Science (MS) in Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science and Electrical Engineering

Year

2022

Author dc:creator

• Tadlock, William M.

• Experimental Analysis of Behavior
• Other Computer Engineering

Statement dc:rights

• Access is available to all users

Repository record dc:identifier

https://dc.ewu.edu/theses/773

OAI identifier oai:identifier

oai:dc.ewu.edu:theses-1775