Classification of Psychiatry Clinical Notes by Diagnosis: A Deep Learning and Machine Learning Approach

TL;DR

This study compares traditional machine learning and deep learning models for classifying psychiatric clinical notes into diagnoses, highlighting the importance of hyperparameter tuning and the limited impact of oversampling techniques.

Contribution

It evaluates the performance of various AI models and oversampling strategies in mental health diagnosis classification, emphasizing hyperparameter tuning's role in accuracy.

Findings

Deep learning and machine learning models achieved 96% accuracy.

Oversampling had minimal overall impact, except SMOTE with BERT models.

Hyperparameter tuning significantly improved model performance.

Abstract

The classification of clinical notes into specific diagnostic categories is critical in healthcare, especially for mental health conditions like Anxiety and Adjustment Disorder. In this study, we compare the performance of various Artificial Intelligence models, including both traditional Machine Learning approaches (Random Forest, Support Vector Machine, K-nearest neighbors, Decision Tree, and eXtreme Gradient Boost) and Deep Learning models (DistilBERT and SciBERT), to classify clinical notes into these two diagnoses. Additionally, we implemented three oversampling strategies: No Oversampling, Random Oversampling, and Synthetic Minority Oversampling Technique (SMOTE), to assess their impact on model performance. Hyperparameter tuning was also applied to optimize model accuracy. Our results indicate that oversampling techniques had minimal impact on model performance overall. The only exception was SMOTE, which showed a positive effect specifically with BERT-based models. However, hyperparameter optimization significantly improved accuracy across the models, enhancing their ability to generalize and perform on the dataset. The Decision Tree and eXtreme Gradient Boost models achieved the highest accuracy among machine learning approaches, both reaching 96%, while the DistilBERT and SciBERT models also attained 96% accuracy in the deep learning category. These findings underscore the importance of hyperparameter tuning in maximizing model performance. This study contributes to the ongoing research on AI-assisted diagnostic tools in mental health by providing insights into the efficacy of different model architectures and data balancing methods.