Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	367163	Smart Healthcare Big Data Implementation and Analysis	1	3.0	3	★	Chien-Yueh Lee	EECS(U)	0	0	創新AI和電資學院大研合開

Course Plan：

Instructor	Chien-Yueh Lee　Office Hours
Email	leecy@ntut.edu.tw
Last Updated	2026-08-27 07:30:11
Course Objective	本課程旨在介紹智慧醫療領域中常見之生醫大數據分析技術與應用，培養學生運用資訊科技解決醫療與生命科學問題之能力。課程內容涵蓋基礎生物學概念、生物資訊的經典演算法、基因體學與轉錄體學資料分析流程，以及微陣列（Microarray）、次世代定序（Next-Generation Sequencing, NGS）等高通量生物技術。學生將學習DNA定序分析、RNA定序分析及總體基因體（Metagenomics）分析方法，並透過實作熟悉生醫大數據分析流程。此外，課程邀請生醫領域專家進行專題演講，介紹電子病歷（Electronic Health Records, EHR）、基因體學（Genomics）、代謝體學（Metabolomics）等智慧醫療大數據之實務應用與發展趨勢。透過案例分析與閱讀期刊討論，培養學生整合生醫資料與人工智慧技術之能力。 This course provides a comprehensive introduction to biomedical big data analytics and its applications in smart healthcare. Topics include fundamental biological concepts, classical bioinformatics algorithms, genomic and transcriptomic data analysis, and high-throughput technologies such as Microarray and Next-Generation Sequencing (NGS). Students will learn the principles and practical workflows of DNA-seq, RNA-seq, and metagenomic analyses through hands-on exercises and real-world datasets. The course also features invited lectures from biomedical experts covering emerging applications of healthcare big data, including Electronic Health Records (EHRs), Genomics, Metabolomics, and other multi-omics data. Through case studies, journal paper discussions, and practical projects, students will gain experience in integrating biomedical data analytics with artificial intelligence techniques to address challenges in healthcare, precision medicine, and biomedical research.
Course Schedule (Week 1-16)	Week 1: Introduction Week 2-3: Concept of biology Week 4: NO CLASS (Teachers' Day) Week 5: Concept of biology Week 6: Special lecture Week 7: Sequencing alignment & Microarray technologies Week 8: NO CLASS (Taiwan Retrocession Day) Week 9: Introduction of sequencing technologies Week 10-11: DNA-seq analysis Week 12-13: RNA-seq analysis Week 14-15: Metagenomic analysis Week 16-17: Special lectures Week 18: Final report
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) ● 遠距教學(Distance learning) Content (1)第17週：邀請專家學者進行遠距教學，講授與智慧醫療相關臨床應用，每位學生於課後提出一份心得報告。(Week 17: Invite experts or scholars to conduct remote lecture on clinical applications related to smart healthcare. Each student is required to submit a reflection report after class.) (2)第18週：各組選定一篇與課程相關之近期研究期刊進行報告分享，全班參與Q&A提問討論。(Week 18: Each group selects a recent research article related to the course for a presentation and sharing session. The entire class participates in a Q&A discussion.) Hours 6 Learning Outcomes (1)第17週：上課後每位學生需繳交心得報告一份。(Week 17: Each student is required to submit a reflection report after class.) (2)第18週：上課前需繳交PPT簡報檔。(Week 18: The PowerPoint presentation file must be submitted before class.) Assessment Percentage (1)心得報告：10% (Reflection report: 10%) (2)口頭報告與PPT簡報：30% (Oral presentation and PPT presentation file: 30%)
Evaluation and grading policy	Practical group exercise Group journal presentation Reflection report Class attendance & involvement
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 1. Campbell Biology 12/e, Lisa U. et al., Pearson. 2. Concepts of Biology, Samantha Fowler et al., OpenStax. 3. Biology (2/e), Mary A. Clark et al., OpenStax. 4. Bioinformatics: A Practical Guide to Next Generation Sequencing Data Analysis, Hamid D. Ismail, CRC Press. 5. Bioinformatics: A Practical Guide to NCBI Databases and Sequence Alignments, Hamid D. Ismail, CRC Press. 6. 生物人的電腦教室,李鎮宇 (Eric Lee), 碁峰資訊 7. Developing Bioinformatics Computer Skills, Per Jambeck & Cynthia Gibas, O'Reilly.
The access to curricular consultation	You can consult about the course in the Online Discussion of iSchool+. If you prefer to discuss privately, it's recommended to use email or arrange an in-person meeting during the teacher's consultation hours.
Expanding teaching and resources	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment. ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
The course corresponds to the SDGs	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
Note	EMI Course