CS372 Artificial Intelligence for Disease Diagnosis

and Information Recommendations

Spring 2020 --- SARS-CoV-2 (COVID-19) Edition


3 units/20 sessions, including lectures and project discussion/presentation.


Edward Chang (echang@cs.stanford.edu)

Adjunct Professor, Computer Science, Stanford University

President, DeepQ Healthcare (areas: AI and healthcare)

Technical Advisor, SmartNews (areas: NLP and NLU)


TA: Saahil Jain (sj2675@cs.stanford.edu)





(3/18/20) Due to the COVID-19 pandemic, the 2020 edition of this course will focus on addressing COVID-19 prediction, containment, and mitigation

 (in addition to covering cancer, depression, and common diseases), using statistical models and AI methods.

(3/29/20) For COVID-19 advice and information, please visit CDC & NIH Websites.

o Get the latest public health information from CDC: https://www.coronavirus.gov

o Get the latest research information from NIH: https://www.nih.gov/coronavirus.



      Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks.  However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago.  This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, subspace learning for enabling interpretability, and privacy-preserving data management.  


The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) methods for COVID-19 path prediction and immunity analysis. Feel free to bring your ideas.


Level: CS senior/graduate, proficient in programming

Perquisite: Introductory course in AI, Statistics, or Machine Learning


Time and Location


·   Time: Tuesdays and Thursdays 9:00am to 10:20am, starting on April 7th.

·   Location: On Zoom (link is provided at Stanford AXESS, public access is prohibited)




·      No exams.

·      Two assignments in the first half of the quarter.

·      Reading: A list of papers/articles/podcasts grouped by topics.  Each student selects two topics of interest to conduct in-depth surveys and presentation.

·      Term project: An extensive design and coding project.  Some example projects are:

a.     COVID-19 symptom checker.

b.     COVID-19 outbreak tracking and prediction using data driven approach.

c.      Thoracic disease diagnosis with limited training data.

d.     A Virtual Assistant for news and/or COVID-19 information recommendations.

e.     Methods for fusing knowledge and perception with deep learning.




·      Assignments 10%

·      Participation/Discussion 10%

·      Term Project 80% broken down into five factors

o   Proposal (functional specifications) 10%

o   Literature survey and algorithm design 10%

o   Implementation & Demo quality 40%

o   Presentation/documentation 10%

o   Effort 10% (ranked within a team)






Course Materials



Deep Learning in Healthcare

and Course Logistics


[1] Schwartz, W. B., R. S. Patil, and P. Szolovits. "AI in Medicine: where do we stand." New England Journal of Medicine 316 (1987): 685-688 [link].

[2] Edward Y. Chang, et al., "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI (2018).

[3] Edward Y. Chang, et al., "Artificial Intelligence in XPRIZE DeepQ Tricorder. " ACM MM Workshop for Personal Health and Health Care (2017).



[1] Small training data challenge;

[2] COVIS-19, What are we facing?  Contrarian opinions? What can AI help?


[1] The next outbreak? We’re not ready | Bill Gates [link], April 2015.

[2] What is a Virus, Vincent Racaniello,Columbia University [link].

[3] What is COVID-19 [link].

[4] Virus Classification, the Baltimore System [link].

[5] Coronavirus: Why You Must Act Now? [link].

[6] How to act? Cost and benefit of social distancing [link][link].

[7] An infected ICU doctor’s frontline experience [link].

Assignment #1:

COVID-19 Analytical Models & Mitigation Strategies

[Please visit Canvas]

Deadline 4/18


The Science of COVID-19: virus, infection cycle, models, mitigation strategies, and data sources


[1] Exponential growth and Epidemics [link].

[2] Estimating actual COVID-19 cases [link].

[3] COVID-19 public dataset program [link].

[4] COVID-19: Predicting the Path and Analyzing Immunity [link].

[5] IDM’s innovative software tools provide a quantitative and analytical means to model infectious disease [link].

[6] Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand, London Imperial College [link].

[7] How much ‘normal’ risk does COVID-19 represent? [link]


Multimodal NLP: Word2vec, Transformers, BERT, GPT, Reformers, and beyond


[1] Attention is all you need, Ashish Vaswani, et al., [link].

[2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [link].

[3] Reformer: the Efficient Transformer [link].                

Assignment #2:

COVID-19 Vaccines and Treatments

[Please visit Canvas]

Deadline 4/28


Tutorials on term projects, datasets and resources


Suggested Topics:

[1] Classifying thoracic diseases on small data (Jason Chou, DeepQ)

[2] Document/news classification on small training data w/ OOD challenge (Jim Deng, SmartNews)

[3] COVID-19 virtual assistant (Stanford OVAL)

[4] COVID-19 simulation modeling

[5] Your own project(s)

Assignment #3:

Term Project


Due on 4/30


Project Q&As


COVID-19 Vaccines and Antivirals


[1] COVID-19 Vaccines (link listed on assignment #2)

[2] COVID-19 Antivirals (link listed on assignment #2)

[3] Milken Institute COVID-19 treatment and vaccine tracker [link].

[4] The science behind COVID-19, Series II, Patrick Soon-Shiong  [link].

[5] The first modern pandemic, Gates Notes, published today (4/23/2020) [link].



Project Proposal Presentation


Healthcare Challenges and Opportunities for a Paradigm Shift



Each includes 1) key objective, 2) related work,

3) proposed methods, and 4) data sources.


[1] Ted Talk on ”Future of Healthcare” by Edward Y. Chang, Taiwan, December 2017 (in Mandarina) [Link]

[2] AI joins the fight against COVID-19, Op-Med, published today (4/28/2020) [Link].


Principles of Diagnosis,

Models and Algorithms


[1] Edward Y. Chang et al., "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI (2018).

[2] Edward Y. Chang et al., "REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis." NIPS (2018).

[3] Szolovits, P., and S. G. Pauker. "Categorical and Probabilistic Reasoning in Medical Diagnosis.” Artificial Intelligence 11(1-2), 1978: 115-144.

[4] Wu, T. D. "Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach." Proceedings of AAAI, 1990, pp. 357-364.

[5] Patil, R. S., P. Szolovits, and W. B. Schwartz. "Causal Understanding of Patient Illness in Medical Diagnosis." In Proceedings of the Seventh International Joint Conference on Artificial Intelligence.



Cancer Causes and Diagnosis


[1] Principles and methods of integrative genomic analyses in cancer, Vessela N. Kristensen, Ole Christian Lingjærde, Hege G. Russnes, Hans Kristian M. Vollan, et al., Nature Reviews, 2014.

[2] Biomarker development in the precision medicine era: lung cancer as a case study, Ashley J. Vargas, and Curtis C. Harris, Natural Reviews, 2016.

[optional] Artificial intelligence in radiology, Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz,and Hugo J. W. L. Aerts, Nature Reviews, 2018.

Dr. Melissa Ko,

PhD, Cancer Biology,



AI for Psychiatry: Beyond Brainless and Mindless?


[1] Students Face Mental Health Challenges --- barriers to care (during COVID-19)”, Stanford Daily (4/30/2020) [link].

[2]Meditation Music: Sangchhen Dorji Lhuendrup Nunnery, Bhutan, Himalayas, Edward Y. Chang [link].

Dr. Vikas Duvvuri,

MD/PhD Stanford



Cancer Treatment and How AI May Help


[1] Mass cytometry: blessed with the curse of dimensionality, Evan W Newell & Yang Cheng, Nature Immunology, June 2016.

[2] Next-Generation Machine Learning for Biological Networks, Diogo M. Camacho, Katherine M. Collins, Rani K. Powers, James C. Costello, and James J. Collins, Leading Edge Review, June 2018.

[optional] Personalized Cancer Models for Target, Discovery and Precision Medicine, Carla Grandori1 and Christopher J. Kemp, Trends in Cancer, CellPress Reviews, September 2018.

Dr. Melissa Ko,

PhD, Cancer Biology,



Project: literature surveys and algorithm reviews

9am: session #1 [video]

2pm: session #2 [on Axess]


Lecture: Explainable/Interpretable Deep Learning [Slides]

[1] Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019). From recognition to cognition: Visual commonsense reasoning. In Proceedings of the IEEE Conference on Computer Vision

and Pattern Recognition, pp. 6720–6731.

[2] Bahri, Y., Kadmon, J., Pennington, J., Schoenholz, S. S., Sohl-Dickstein, J., & Ganguli, S. (2020). Statistical mechanics of deep learning. Annual Review of Condensed Matter Physics.


AI Assisted Coding and Debugging (Ask AI to code your project!)

[video][Slides (NA)]


Project review

2pm: session #3 [on Axess]

[1] Code search work [link]. 

[2] Code recommendation work [link].

[3] Automatic bug fixing [link].

[4] Predictive test selection [link].

Eric Meijer,


Interpretable and robust AI: Successive Subspace Learning (SSL), Forward-Only Feature Learning


[1] C.-C. Jay Kuo, “Understanding convolutional neural networks with a mathematical model,” the Journal of Visual Communications and Image Representation, Vol. 41, pp. 406-413, 2016.

[2] C.-C. Jay Kuo, Min Zhang, Siyang Li, Jiali Duan and Yueru Chen, “Interpretable Convolutional Neural Networks via Feedforward Design,” the Journal of Visual Communications and Image Representation, Vol. 60, pp. 346-359, 2019.

[3] M. Zhang, H. You, P. Kadam, S. Liu, C.-C. J. Kuo, “PointHop: an explainable machine learning method for point cloud classification,” arXiv:1907.12766 (2019), IEEE Trans. on Multimedia.

[4] Yueru Chen and C.-C. Jay Kuo, “PixelHop: a successive subspace learning (SSL) method for object classification,” arXiv:1909.08190 (2019), Visual Communications and Image Representation.

Prof. C.-C Jay Kuo,



Data Privacy and Regulations Compliance           



[2] Soteria: A Provably Compliant User Right Manager Using a Novel Two-Layer Blockchain Technology, Stanford University, DeepQ, NTU, Edward Y. Chang el al., 2020 [link].


AI Future, novel topics in

Deep Learning, NLP, and Healthcare

[video][Slides: abridged version]

[1] Panel: VR/AR for Surgery and Medical Education, April 2018 [link].

[2] Stanford SNI Talk: Advancing Healthcare w/ AI and VR, Edward Y. Chang [link] (Stanford ID required).

[3] From system 1 to system 2, keynote, Yoshua Bengio, NeurIps 2019 [link].

[4] Daphne Koller:  Biomedicine and Machine Learning, AI Podcast #93 with Lex Fridman, May 2020 [link].



An Uncertain Future: Coping with COVID-19 anxiety with facts and right perspectives


This pandemic is not going away soon.  It has impact health, economy, and living tremendously, perhaps the most drastic one in our lifetime this far.  How should we deal with it mentally?  Dr. Duvvuri lectures on COVID-19 and AI.


Dr. Vikas Duvvuri,

MD/PhD Stanford


Extended Office Hours

Schedule a time slot by appointment between 9am and 3pm

Organized by TA

6/9: 9am – 10:20am

Project: final presentations

Session #1 [video]

[1] Project #1, Learning Metastatic Features of Tumor Microenvironment

[2] Project #2, Thoracic Disease Detection using Transfer Learning and Meta Learning


Final report due on 6/11

6/9: 2pm – 3:20pm

Project: final presentations

Session #2 [Included in the video above]

[1] Project #3, COVID-19 modeling with regression

[2] Project #4, Document classification/summarization with small data

Final report due on 6/11