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rail [2024/03/17 15:34]
nigam [Creation and adoption of foundation models in medicine] |
rail [2024/03/17 16:06]
nigam [Making machine learning models clinically useful] |
| ===== Making machine learning models clinically useful ===== | ===== Making machine learning models clinically useful ===== |
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| Whether a classifier or prediction [[ https://jamanetwork.com/journals/jama/article-abstract/2748179 | model is usefulness]] in guiding care depends on the interplay between the model's output, the intervention it triggers, and the intervention’s benefits and harms. | Whether a classifier or prediction [[ https://jamanetwork.com/journals/jama/article-abstract/2748179 | model is usefulness]] in guiding care depends on the interplay between the model's output, the intervention it triggers, and the intervention’s benefits and harms. Our work stemmed from the effort [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html|in improving palliative care]] using machine learning. [[https://www.tinyurl.com/hai-blogs | Blog posts at HAI]] summarize our work in easily accessible manner. |
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| {{ :model-interplay.png?400&nolink& }} | {{ :model-interplay.png?400&nolink& }} |
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| We study this interplay to inform the work of the [[https://dsatshc.stanford.edu/ | Data Science Team at Stanford Healthcare]]. Our work stemmed from the effort [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html|in improving palliative care]] using machine learning. [[https://www.tinyurl.com/hai-blogs | Blog posts at HAI]] summarize our work in easily accessible manner. [[https://jamanetwork.com/journals/jama/fullarticle/2748179?guestAccessKey=8cef0271-616d-4e8e-852a-0fddaa0e5101|Ensuring that machine learning models are clinically useful]] requires quantifying the [[http://academic.oup.com/jamia/article/28/6/1149/6045012|impact of work capacity constraints]] on achievable benefit, estimating [[https://www.sciencedirect.com/science/article/pii/S1532046421001544|individualized utility]], and learning [[https://pubmed.ncbi.nlm.nih.gov/34350942/|optimal decision thresholds]]. | We study how to quantify the [[https://www.sciencedirect.com/science/article/pii/S1532046423000400|impact of work capacity constraints]] on achievable benefit, estimate [[https://www.sciencedirect.com/science/article/pii/S1532046421001544|individualized utility]], and learn [[https://pubmed.ncbi.nlm.nih.gov/34350942/|optimal decision thresholds]]. We question conventional wisdom on whether models [[https://tinyurl.com/donot-explain | need to be explainable]], and [[https://www.nature.com/articles/s41591-023-02540-z |generalizable]]. We examine if consequences of using [[https://hai.stanford.edu/news/when-algorithmic-fairness-fixes-fail-case-keeping-humans-loop | algorithm guided care are fair]] and how to [[https://hai.stanford.edu/news/how-do-we-ensure-healthcare-ai-useful | ensure that healthcare models are useful]]. We study this interplay to guide the work of the [[https://dsatshc.stanford.edu/ | Data Science Team at Stanford Healthcare]]. |
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