本課程旨在引導學生深入認知障礙，並結合資料科學方法進行跨領域的實務應用。
一、課程目標（Course Objectives）
學生修習本課程後，應能：
理解核心知識：掌握認知障礙的定義、主要分類、成因及危險因子。
聽證與評估：學習聽證障礙的常見症狀，並熟悉其標準評估方法與診斷流程。
了解對策：認識策略認知障礙的主要治療方式與長期管理策略。
應用資料分析：能夠運用機器學習等資料分析方法，對認知障礙相關資料集（如MRI影像、失智症資料、TRODAT掃描）進行初步探討與分析。
培養研究能力：透過分組專題，學習獨立或合作進行主題研究，並能有效呈現與溝通研究成果。
二、課程內涵（課程內容）
認知障礙學理基礎：
由專業醫師介紹認知功能障礙的學術理論，探討其作為影響與工作能力的綜合性症狀（如記憶力、語言能力、計算、判斷、抽象思考、空間感知、注意力等的相關性變化）。
深入解析故障的多元成因、危險因子、臨床表徵及指紋診斷標準。
數據科學在認知障礙的應用：
介紹如何運用數學原理與程式語言（結合人工智慧/機器學習方法）分析認知功能障礙相關數據。
強調資料分析在認知障礙研究與臨床應用的跨領域價值。
專題實踐與研究發表：
學生將分組，分別針對以下主題進行資料實務分析與專題研究：
MRI影像分析組：探討腦部結構與功能影像在認知障礙研究的應用。
失智症資料分析組：分析臨床失智症相關數據，探索潛在模式或關聯性。
課程引導各組學生學習實際資料處理、模型近期與成果展望，目標是能夠獨立或合作完成專題研究，並鼓勵將研究成果整理成供發表論文的形式（如學術期刊）。This course aims to guide students to deeply understand obstacles and combine data science methods for cross-domain practical applications.
1. Course Objectives
After students take this course, they should be able to:
Understand core knowledge: master the definitions, main categories, causes and risk factors of cognitive impairment.
Audit and evaluation: Learn common symptoms of hearing disorders and be familiar with their standard evaluation methods and diagnosis procedures.
Understand the solutions: Awareness strategies The main treatment methods and long-term management strategies for cognitive impairment.
Application data analysis: Can use machine learning and other data analysis methods to conduct preliminary exploration and analysis of data sets related to cognitive impairment (such as MRI images, dementia data, TRODAT scan).
Cultivate research ability: learn to conduct topic research independently or cooperatively through the grouping of topics, and effectively present research results with communication.
2. Connotation of course (course content)
Understanding the basics of obstacles:
A professional doctor introduces the academic discussion of cognitive dysfunction and explores its combined symptoms of influence and work ability (such as the correlation changes in memory, language ability, calculation, judgment, abstract thinking, spatial perception, attention, etc.).
In-depth analysis of the multiple causes, risk factors, clinical symptoms and fingerprint diagnosis standards of failures.
Application of data science in cognitive impairment:
Introduce how to use mathematical principles and programming language (combined with artificial intelligence/machine learning methods) to analyze and recognize dysfunction-related data.
Strengthen the cross-domain value of data analysis in cognitive impairment research and clinical application.
Subject practical and research published:
Students will be divided into groups to conduct data practical analysis and topic research on the following topics:
MRI image analysis group: Exploring the application of brain structure and functional images in cognitive impairment research.
Dementia data analysis group: Analyze clinical dementia-related data and explore potential patterns or correlations.
The course guides students to learn practical data processing, model recent and outcomes, with the goal of being able to complete topical research independently or in cooperation, and encourage the organization of research results into forms for publication (such as academic journals).
1. 失智症診療手冊 第三版 衛生福利部 (2017)
2. ABC of Dementia
作者:Coope
出版商:Wiley-Blackwell
出版年代/版次 2020/ 2
ISBN/ 9781119599395
3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
作者:Aurélien Géron
出版商:O'Reilly Media, Inc.
出版年代/版次 September 2019/ 2
ISBN/ 9781492032649
4. The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images. Ni YC, Tseng FP, Pai MC, Hsiao IT, Lin KJ, Lin ZK, Lin CY, Chiu PY , Hung GU, Chang CC, Chang YT, Chuang KJ and Alzheimer’s Disease Neuroimaging Initiative. Diagnostics 2021, 11(11), 2091; https://doi.org/10.3390/diagnostics11112091.
5. Yang YW, Hsu KC, Wei CY, Tzeng RC, Chiu PY*. Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale. Front. Aging Neurosci., 07 September 2021 | https://doi.org/10.3389/fnagi.2021.705782. (* Corresponding author)
6. Chang YF, Loi WY, Chiu PY*, Huang HN. Classification of Dementia Severity in Taiwan Based on History-Based Clinical Diagnosis System. Am J Alzheimers Dis Other Demen. 2020 Jan-Dec;35:1533317520970788. doi: 10.1177/1533317520970788. (* Corresponding author)
1. Dementia Clinic Manual Third Edition Department of Health and Welfare (2017)
2. ABC of Dementia
Author: Coope
Publisher: Wiley-Blackwell
Published Year/Page 2020/ 2
ISBN/ 9781119599395
3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Author: Aurélien Géron
Publisher: O'Reilly Media, Inc.
Published Year/Page September 2019/ 2
ISBN/ 9781492032649
4. The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images. Ni YC, Tseng FP, Pai MC, Hsiao IT, Lin KJ, Lin ZK, Lin CY, Chiu PY , Hung GU, Chang CC, Chang YT, Chuang KJ and Alzheimer’s Disease Neuroimaging Initiative. Diagnostics 2021, 11(11), 2091; https://doi.org/10.3390/diagnostics11112091.
5. Yang YW, Hsu KC, Wei CY, Tzeng RC, Chiu PY*. Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale. Front. Aging Neurosci., 07 September 2021 | https://doi.org/10.3389/fnagi.2021.705782. (* Corresponding author)
6. Chang YF, Loi WY, Chiu PY*, Huang HN. Classification of Dementia Severity in Taiwan Based on History-Based Clinical Diagnosis System. Am J Alzheimers Dis Other Demen. 2020 Jan-Dec;35:1533317520970788. doi: 10.1177/1533317520970788. (* Corresponding author)
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 出席出席 Attend |
10 | 出席率 |
| 專題實做專題實做 Special topic practice |
70 | 期末實做呈現 |
| 課堂報告與討論課堂報告與討論 Class Report and Discussion |
20 | 課堂的進度報告 |
