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姓名	黃永璿(Yun-shuan Huang)  查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	non (Bipolar Disorder Prediction with Transfer Learning on Acoustic and Linguistic Embeddings)
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檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2029-7-1以後開放)
摘要(中)	躁鬱症是一種精神疾病，對個人的生活有深遠影響。準確且早期的診斷至關重要，然而誤診為抑鬱症可能導致錯誤的治療。開發協助臨床醫生進行精確診斷的工具可以減少誤診的機會。機器學習提供了這樣的解決方案。近年來，機器學習在音訊領域的研究日益增多，許多研究探索了使用音訊資料來預測精神疾病的可能性。然而，要收集到足夠的音訊資料來建立分類模型的成本非常高，這也成為使用音訊作為模型學習資料來源的相關研究中的一大限制。遷移學習提供了一個可行的解決方案來應對小型資料集的問題。本研究針對音訊和文本特徵在診斷躁鬱症的有效性進行了全面研究，比較了傳統的特徵工程技術與預訓練模型所提取的學習特徵。本研究的結果顯示學習特徵顯著優於傳統的特徵工程術。此外，本研究還探討了結合音訊和文本資料多模態方法的成效。雖然這些多模態方法未能超越單獨使用音訊特徵的表現，但它們在單獨使用文本特徵方面提供了顯著的提升。這些發現為未來進行相關研究提供了有價值的方法論參考。
摘要(英)	Bipolar disorder is a mental disorder that can seriously affect individuals. Early and accurate diagnosis is crucial; however, misdiagnosis of bipolar disorder as depression can lead to incorrect treatment. Therefore, it is important to develop tools that support clinicians in making accurate diagnoses. Machine learning approaches can provide such solutions. Recently, audio has become an important domain for research, with increasing studies exploring the use of audio data to predict mental disorders. However, collecting a sufficient amount of audio data to build a classifier model is costly and impractical, presenting a challenge in utilizing audio data. To address the issue of limited datasets, transfer learning offers a viable solution. In this paper, we conduct a comprehensive study on the effectiveness of audio and textual features, comparing conventional hand-crafted features with learned features. Our results show that learned features outperform conventional hand-crafted features. Additionally, we explore multimodal approaches that combine audio and textual data, finding that while multimodal techniques do not surpass the performance of audio features alone, they do provide improvements over textual features alone. These findings highlight the potential of learned features and multimodal approaches in supporting the accurate diagnosis of bipolar disorder, suggesting a promising direction for future research.
關鍵字(中)	★ 躁鬱症 ★ 音訊特徵 ★ 文字特徵 ★ 深度學習特徵 ★ 多模態學習	關鍵字(英)	★ Bipolar Disorder ★ Acoustic Feature ★ Linguistic Feature ★ Learned Feature ★ Multi-modal Learning
論文目次	摘要 i Abstract ii 誌謝 iii List of Figures v List of Tables vi 1. Introduction 1 2. Literature Review 5 2.1 The Investigative Features for Predicting Bipolar Disorder 5 2.1.1 Acoustic (Audio) Features 7 2.1.2. Linguistic (Text) Feature 9 2.2 Transfer Learning 10 3. Research Method 12 3.1 Dataset 12 3.2 Feature Extraction 13 3.2.1 Exploring Low-Level Features 13 3.2.2 Exploring High Level Features 14 3.3 Classification Methods 17 • XGBoost 17 • Random Forest 17 • Logistic Regression 18 • Support Vector Machine 18 • K-Nearest Neighbour 18 3.4 Experiment Design 19 3.5 Evaluation Metrics 22 4. Experiment 24 4.1 Experiment 1: Evaluating the Effectiveness of Acoustic Features in Bipolar Disorder 24 4.3 Experiment 2 26 4.4 Experiment 3 29 4.5 Discussion 31 5. Conclusion 34 Reference 36
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指導教授	胡雅涵(Ya Han Hu)	審核日期	2024-7-22
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