2021 Spring IPH5018 Biomedical NLP

• • This class will introduce basic concept of natural language processing (NLP) and clinical applications of NLP.
  • The class will cover from traditional NLP to up-to-date deep learning-based NLP methods.
  • Highly recommend to take this class after “DHC5031 Introduction to Medical Informatics,” and “DHC5036 Machine Learning in Medicine.“ 
  • This class will consider the students know the intermediate knowledge in machine learning including deep learning and medical informatics.
  • Online only class (real-time streaming)

• • Prof. Soo-Yong Shin (sy.shin (at) skku.edu)

• • Thursday 9:00 AM ~ 11:50 AM
  • Webex: https://skku-ict.webex.com/meet/sy.shin 

• • None

• • Attendance: 10%
  • Presentation: 40%
    • Each student should present single biomedical NLP article
    • Candidate article list
    • Check your schedule on the file
    • You have to send presentation file before your presentation. (ex. every Wednesday)
    • The score will be measured by the presentation (including material) and discussion (when you are not the presenter)
  • Final Exam: 50%
•  
  • Final Grade

• • Final Term introduction

(course schedule can be changed.)

1. 1. 2/25: Course Introduction  & Intro NLP
   2. 3/4: Traditional NLP
   3. 3/11: NLP Example on Pubmed articles
   4. 3/18: Electronic Medical Records: Clinical Notes
   5. 3/25: Regular Expression & Clinical application
   6. 4/1: NLP using ML: Basic ML theory
   7. 4/8: Recurrent Neural Network
   8. 4/15: Sequence2Sequence Model
   9. 4/22: Word2Vec
   10. 4/29: BERT series on Medical Domain
   11. 5/6: Text CNN
   12. 5/13: Recent Clinical Applications (1)
       • BioSentVec: creating sentence embeddings for biomedical texts (by Ji Woong Park)
       • Deep learning in clinical natural language processing: a methodical review (by Jong Hyuk Kim)
       • A study of deep learning approaches for medication and adverse drug event extraction from clinical text (by Yeonghak Bang)
   13. 5/20: Recent Clinical Applications (2)
       • Clinical Text Data in Machine Learning: Systematic Review (by Seohu Kim)
       • Improving Clinical Document Understanding on COVID-19 Research with Spark NLP (by Geunhyeong Lee)
       • ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network (by Yoonho Choi)
   14. 5/27: Recent Clinical Applications (3)
       • Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning (by Dongbin Lee)
       • Clinical concept extraction: A methodology review (by Ji Eun Hwang)
       • Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer (by Hyunwoo Choo)
   15. 6/3: Final Exam