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.
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
