A STUDY ON USING CHIEF COMPLAINTS OF PATIENTS WITH MAJOR DEPRESSIVE DISORDER AND BIPOLAR DISORDER FOR VOICE ANALYSIS AND TEXT MINING TO DIFFERENTIATE BETWEEN THE TWO
Authors: Shen *Cheng Che , Hu Ya-Han , Bai Ya-Mei 
Published: February 2025

Abstract

Background
Major depressive disorder (MDD) and bipolar disorder are common psychiatric mood disorders with distinct clinical trajectories and treatment approaches. Misdiagnosis occurs when bipolar disorder manifests as depression, leading to treatment resistance in MDD. Currently, no method can reliably differentiate between depressive episodes in MDD and bipolar disorder. This study employs text mining, audio analysis, and data mining to develop predictive models for distinguishing these conditions.

Aims & Objectives
The study aims to:
1. Collect data from individuals diagnosed with MDD or bipolar disorder, focusing on patients above 18 years old.
2. Utilize text and audio analysis for feature extraction.
3. Apply data mining techniques to establish predictive models.
4. Evaluate the effectiveness of these models in distinguishing between MDD and bipolar disorder.

Method
Data was sourced from a northern Taiwan medical center, involving individuals diagnosed with MDD or bipolar disorder, with exclusions for schizophrenia or organic brain disorders. Clinical diagnoses served as the dependent variable for predictive models. The dataset included basic patient information and audio recordings in which patients described their recent emotional states and life situations.
Data preprocessing involved the conversion of questionnaire responses into variable values, audio-to- text transcription, and manual adjustments for alignment. Text preprocessing included tokenization, part-of-speech tagging, and stop word removal. The extraction of feature values included text feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) method for identifying feature words and sentiment analysis. Audio features were extracted using Cool Edit Pro 2.1 software.
The study employed seven data mining techniques, such as decision trees, k-nearest neighbors, gradient boosting, logistic regression, Naive Bayes, random forest, support vector machine, and XGBoost, for model development and analysis using Orange 3. To mitigate overfitting, a 5-fold cross-validation process divided the dataset into five subsets for robust model evaluation. An undersampling method addressed class imbalance using the SpreadSubsample module in Weka 3.8.3, and model performance was assessed using confusion matrices.

Results
The study gathered data from 37 cases, comprising 20 MDD and 17 bipolar disorder diagnoses. Independent variables included 107 textual content features, 6 audio features, and 10 patient information variables. Patients with bipolar disorder had higher psychiatric hospitalization histories than those with MDD (table 1).
The decision tree model exhibited the highest sensitivity (1.000), AUC (0.875), accuracy (0.879), and f1-measure (0.889), while the logistic regression model had the highest precision (1.000) (table 2).

Conclusion
Differentiating between MDD and bipolar disorder is vital for tailored treatments and patient outcomes. This study pioneers the use of Chinese case narratives to predict depressive disorder or bipolar disorder, with the decision tree model showing promising predictive capability (AUC 0.875). However, the small sample size necessitates further validation with a larger cohort to confirm the model's efficacy. This research offers potential for improved diagnostic accuracy and personalized treatment in mood disorders.

References
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