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HST.953 | Fall 2024 | Graduate

# Clinical Data Learning, Visualization, and Deployments

### Week 1

Ghassemi, M., Naumann, T., et al. “A Review of Challenges and Opportunities in Machine Learning for Health.” AMIA Jt Summits Transl Sci Proc. (2020) May 30: 191–200. PMID: 32477638; PMCID: PMC7233077.

Chen, I.Y., Pierson, E., et al. “Ethical Machine Learning in Healthcare.” Annual Review of Biomedical Data Science, Vol. 4(2021): 123–44.

Wiens, J., Saria, S., Sendak, M., et al. Author Correction: “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nat Med 25, 1627 (2019). https://doi.org/10.1038/s41591-019-0609-x

### Week 2

Johnson, A., Pollard, T., Shen, L., et al. “MIMIC-III, a Freely Accessible Critical Care Database.” Sci Data 3, 160035 (2016). https://doi.org/10.1038/sdata.2016.35

McDermott, M. B. A., et al. “Reproducibility in Machine Learning for Health Research: Still a Ways to Go."Sci. Transl. Med. 13, eabb1655(2021). DOI:10.1126/scitranslmed.abb1655

Extra Material:

Deep Dive into MIMIC-IV

Johnson, A. “Part 1: Analyzing Critical Care Data, from Speculation to Publication, Starring MIMIC-IV.” July 23, 2020. SlidesLive.

Colab. “mimic-iv-tutorial.ipynb” July 24, 2020. colab.research.google.com

Going through a study done in MIMIC-IV

Johnson, A.. “Part 2: Analyzing Critical Care Data, from Speculation to Publication, Starring MIMIC-IV.” July 23, 2020. SlidesLive.

alistairewj. “mimic-iv-aline-study” Oct 19, 2021. GitHub.

### Week 3:

Che, Z., Purushotham, S., et al. “Recurrent Neural Networks for Multivariate Time Series with Missing Values.” Scientific Reports 8, no. 1 (2018): 6085.

Boecking, B., Usuyama, N., et al. “Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing.” In European Conference on Computer Vision, pp. 1-21. Cham: Springer Nature Switzerland, 2022.

Huang, S.C., Pareek, A., et al. “Multimodal Fusion with Deep Neural Networks for Leveraging CT Imaging and Electronic Health Record: A Case-Study in Pulmonary Embolism Detection.” Sci Rep 10, 22147 (2020). https://doi.org/10.1038/s41598-020-78888-w

### Week 4:

Gotz, D., and Borland, D. “Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization.” IEEE computer graphics and applications 36, no. 3 (2016): 90–96.

Jin, Z., Cui, S., et al. “CarePre: An Intelligent Clinical Decision Assistance System.” ACM Trans. Comput. Healthcare 1, no. 1 (2020): 1–20. https://doi.org/10.1145/3344258

Glueck, M., Pakdaman Naeini, M., et al. “PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models.” IEEE Transactions on Visualization and Computer Graphics 24, no. 1 (2017): 371–381.

Pierson, E., Cutler, D.M., et al. “An Algorithmic Approach to Reducing Unexplained Pain Disparities in Underserved Populations.” Nat Med 27(2021): 136–140. https://doi.org/10.1038/s41591-020-01192-7

### Week 5:

Tucker, A., Wang, Z., Rotalinti, Y., et al. “Generating High-Fidelity Synthetic Patient Data for Assessing Machine Learning Healthcare Software.” npj Digit. Med. 3, 147 (2020). https://doi.org/10.1038/s41746-020-00353-9

Stadler, T., Oprisanu, B., and Troncoso, C.. “Synthetic Data–Anonymisation Groundhog Day.” In 31st USENIX Security Symposium (USENIX Security 22), pp. 1451–1468. 2022.

### Week 6:

Tang, S., Makar, M., et al. “Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare.” Advances in Neural Information Processing Systems 35 (2022): 34272–34286.

Tang, S., Modi, A., et al. “Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies.” Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:9387-9396, 2020.

Zhang, J., Budhdeo, S., et al*.* “Moving Towards Vertically Integrated Artificial Intelligence Development.” npj Digit. Med. 5, 143 (2022). https://doi.org/10.1038/s41746-022-00690-x

### Week 7:

Bastani, H., Drakopoulos, K., et al. “Interpretable Operations Research for High-Stakes Decisions: Designing the Greek COVID-19 Testing System.” INFORMS Journal on Applied Analytics 52, no. 5(2022): 398–411.

Bastani, H. et al. “Efficient and Targeted COVID-19 Border Testing via Reinforcement Learning.” Nature vol. 599,7883 (2021): 108-113. doi:10.1038/s41586-021-04014-z

Antoniou, T., and Mamdani, M. “Evaluation of Machine Learning Solutions in Medicine.” CMAJ 193 no. 36(2021): E1425–E1429; doi:10.1503/cmaj.210036

Cohen, J.P., Cao, T., et al. “Problems in the Deployment of Machine-Learned Models in Health Care.” CMAJ 193 no. 35(2021):E1391–E1394. doi:10.1503/cmaj.202066

Verma, A.A., Murray, J., et al. “Implementing Machine Learning in Medicine.” CMAJ 193 no. 34(2021):E1351–E1357. doi:10.1503/cmaj.202434

### Week 8:

Rajkomar, A., Dean, J., et al. “Machine Learning in Medicine.” N Engl J Med. 380 no. 14(2019): 1347–1358. doi:10.1056/NEJMra1814259

Finlayson, S.G., Subbaswamy, A., et al. “The Clinician and Dataset Shift in Artificial Intelligence.” N Engl J Med. 385 no. 3(2021): 283-286. doi:10.1056/NEJMc2104626

Norgeot, B., Quer, G., Beaulieu-Jones, B.K., et al. “Minimum Information About Clinical Artificial Intelligence Modeling: The MI-CLAIM Checklist.” Nat Med. 26 no. 9(2020): 1320–1324. doi:10.1038/s41591-020-1041-y

### Week 9:

Gawande, A “Why Doctors Hate Their Computers.” The New Yorker. November 5, 2018.

Overview of UK Biobank

Chen, I.Y., Pierson, E., et al. “Ethical Machine Learning in Healthcare.” Annual Review of Biomedical Data Science 4(2021):123–144.

### Week 10:

Cutler, D.M. “What Artificial Intelligence Means for Health Care.” JAMA Health Forum. 2023;4(7):e232652. doi:10.1001/jamahealthforum.2023.2652

Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., et al. “Revolutionizing Healthcare: The Role of Artificial Intelligence in Clinical Practice.” BMC Med Educ 23, 689 (2023). doi.org/10.1186/s12909-023-04698-z

Robeznieks, A. “AI Is Already Reshaping Care. Here’s What It Means for Doctors.” American Medical Association (AMA), Apr 5, 2024.

### Week 12:

Ghasemi, A., Hashtarkhani, S., et al. “Explainable Artificial Intelligence in Breast Cancer Detection and Risk Prediction: A Systematic Scoping Review.” Cancer Innovation 3, no. 5(2024): e136. doi.org/10.1002/cai2.136

Karimian, G., Petelos, E., et al. “The Ethical Issues of the Application of Artificial Intelligence in Healthcare: A Systematic Scoping Review.” AI Ethics 2 (2022): 539–551. doi.org/10.1007/s43681-021-00131-7

Matheny, M.E., Whicher, D., et al. “Artificial Intelligence in Health Care: A Report From the National Academy of Medicine.” JAMA. 2020;323(6):509–510. doi:10.1001/jama.2019.21579

## Course Info

##### Instructors

- Marzyeh Ghassemi
- Leo A Celi
- Adam Rodman
- Ned McCague

##### Departments

- Electrical Engineering and Computer Science
- Health Sciences and Technology

##### As Taught In

Fall 2024

##### Level

Graduate

##### Topics

- Engineering
  - Computer Science
    - Artificial Intelligence
- Health and Medicine

##### Learning Resource Types

assignment Problem Sets
assignment Programming Assignments
auto_stories Readings

Browse Resources

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