Clinical Data Learning, Visualization, and Deployments

Readings

• 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. ↗
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• Chen, I.Y., Pierson, E., et al. “ Ethical Machine Learning in Healthcare .” Annual Review of Biomedical Data Science, Vol. 4(2021): 123–44. ↗
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• 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 ↗
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• 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 ↗
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• 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 ↗
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• Extra Material:
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• Deep Dive into MIMIC-IV
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• Johnson, A. “ Part 1: Analyzing Critical Care Data, from Speculation to Publication, Starring MIMIC-IV .” July 23, 2020. SlidesLive. ↗
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• Colab. “ mimic-iv-tutorial.ipynb ” July 24, 2020. colab.research.google.com ↗
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• Going through a study done in MIMIC-IV
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• Johnson, A.. “ Part 2: Analyzing Critical Care Data, from Speculation to Publication, Starring MIMIC-IV .” July 23, 2020. SlidesLive. ↗
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• alistairewj. “ mimic-iv-aline-study ” Oct 19, 2021. GitHub. ↗
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• Che, Z., Purushotham, S., et al. “ Recurrent Neural Networks for Multivariate Time Series with Missing Values .” Scientific Reports 8, no. 1 (2018): 6085. ↗
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• 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. ↗
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• 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 ↗
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• 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. ↗
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• 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 ↗
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• 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. ↗
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• 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 ↗
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• 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 ↗
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• Stadler, T., Oprisanu, B., and Troncoso, C.. “ Synthetic Data–Anonymisation Groundhog Day .” In 31st USENIX Security Symposium (USENIX Security 22) , pp. 1451–1468. 2022. ↗
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• 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. ↗
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• 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. ↗
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• 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 ↗
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• 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. ↗
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• 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 ↗
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• Antoniou, T., and Mamdani, M. “ Evaluation of Machine Learning Solutions in Medicine .” CMAJ 193 no. 36(2021): E1425–E1429; doi:10.1503/cmaj.210036 ↗
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• 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
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• Verma, A.A., Murray, J., et al. “ Implementing Machine Learning in Medicine .” CMAJ 193 no. 34(2021):E1351–E1357. doi:10.1503/cmaj.202434 ↗
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• Rajkomar, A., Dean, J., et al. “ Machine Learning in Medicine .” N Engl J Med . 380 no. 14(2019): 1347–1358. doi:10.1056/NEJMra1814259 ↗
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• 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 ↗
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• 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 ↗
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• Gawande, A “ Why Doctors Hate Their Computers .” The New Yorker. November 5, 2018. ↗
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• Chen, I.Y., Pierson, E., et al. “ Ethical Machine Learning in Healthcare .” Annual Review of Biomedical Data Science 4(2021):123–144. ↗
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• Cutler, D.M. “ What Artificial Intelligence Means for Health Care .” JAMA Health Forum . 2023;4(7):e232652. doi:10.1001/jamahealthforum.2023.2652 ↗
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• 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 ↗
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• Robeznieks, A. “ AI Is Already Reshaping Care. Here’s What It Means for Doctors .” American Medical Association (AMA), Apr 5, 2024. ↗
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• 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 ↗
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• 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 ↗
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• 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 ↗
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