Health-care has turned into a big data-driven domain, which produces a multitude of data, such as structured data in the form of clinical health records, free text in form of case studies and clinical trials, or colloquial discussions shared via social media outlets or patient forums. While this data overload can be a challenge for patients and caretakers, it also enables Machine Learning (ML) approaches to deliver new insights and open new application opportunities. For instance, it enables physicians to retrieve relevant information about disease outbreaks, viruses, etc substantially faster or it facilitates the discovery of phenotype-specific relationships between diseases and drugs to develop personalized medicine. This seminar covers a range of topics, showcasing the application of ML to such use cases. Some relevant topics include: Information Retrieval & Extraction, Conversational Health AI, Health Data Science etc.
07.05.2020 - Introductory Lecture
16.06.2020 - Midterm Meeting with Instructors
16.07.2020 - Report Submission Deadline
06.08.2020 - Review Submission Deadline
20.08.2020 - Final Report Submission Deadline
XX.08.2020 - Day 1 block seminar (TBA)
YY.08.2020 - Day 2 block seminar (TBA)
- Mohammad Alsulmi and Ben Carterette. 2016. Improving clinical case search using semantic based query reformulations. In Bioinformatics and Biomedicine (BIBM'16).
- Sendong Zhao, Chang Su, Andrea Sboner, and Fei Wang. 2019. GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM ’19).
Automatic Health Assessment
- Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. 2019. Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention. In The World Wide Web Conference (WWW ’19).
- Payam Karisani and Eugene Agichtein. 2018. Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media. In Proceedings of the 2018 World Wide Web Conference (WWW ’18).
Social Media Analysis for Health Care
- Kathy Lee, Ashequl Qadir, Sadid A. Hasan, Vivek Datla, Aaditya Prakash, Joey Liu, and Oladimeji Farri. 2017. Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17).
- Zi Chai, Xiaojun Wan, Zhao Zhang, and Minjie Li. 2019. Harvesting Drug Effectiveness from Social Media. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19).
- Nan Du, Kai Chen, Anjuli Kannan, Linh Tran, Yuhui Chen, and Izhak Shafran. Extracting Symptoms and their Status from Clinical Conversations.
- Xuan Wang, Yu Zhang, Qi Li, Yinyin Chen, and Jiawei Han. 2018. Open Information Extraction with Meta-pattern Discovery in Biomedical Literature. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB ’18).
- Bickmore T1, Giorgino Tm, Health dialog systems for patients and consumers. J Biomed Inform. 2006 Oct;39(5):556-71. Epub 2006 Jan 20.
- Tanaka, H., Negoro, H., Iwasaka, H. and Nakamura, S., 2017. Embodied conversational agents for multimodal automated social skills training in people with autism spectrum disorders. PloS one, 12(8).
|Full Name||Primary Topic||Secondary Topic||Instructor|
|Ege Karaaslan||Information Extraction||Conversational AI||Erisa Terolli|
|Moonis Ali||Conversational AI||Information Extraction||Patrick Ernst|
|Thong Nguyen Thac||Automatic Health Assessment||Social Media Analysis||Andrew Yates|
|Aakash Rajpal||Conversational AI||Automatic Health Assessment||Patrick Ernst|
|Neda Foroutan||Automatic Health Assessment||Information Extraction||Andrew Yates|
|Anam Sadiq||Information Extraction||Conversational AI||Patrick Ernst|
|Tim Bruxmeier||Social Media Analysis||Automatic Health Assessment||Erisa Terolli|
|Hafeez Ullah||Automatic Health Assessment||Convolutional AI||Andrew Yates|
|August von Liechtenstein||Information Extraction||Automatic Health Assessment||Erisa Terolli|
At the end of the seminar every student needs to submit one technical report of 8 pages including references and a review for a colleague's technical report assgined by the instructors of not more than 3 pages.
These reports together with an oral presentation will constitute the final grade where :
- Technical Report (50 points)
- Review Report (20 points)
- Oral Presentation (30 points)
Your final grade with be given according to the following rules:
- 1.0 if grade >= 90
- 1.3 if 87 <= grade < 90
- 1.7 if 84 <= grade < 87
- 2.0 if 80 <= grade < 84
- 2.3 if 77 <= grade < 80
- 2.7 if 74 <= grade < 77
- 3.0 if 70 <= grade < 74
- 3.3 if 67 <= grade < 70
- 3.7 if 64 <= grade < 67
- 4.0 if 60 <= grade < 64
- Fail if grade < 60
where grade is the aggregated number of points collected from the reports and the oral presentation.