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Instability in clinical risk stratification models using deep learning.

, , , , , , , , , , , and . ML4H@NeurIPS, volume 193 of Proceedings of Machine Learning Research, page 552-565. PMLR, (2022)

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Underspecification Presents Challenges for Credibility in Modern Machine Learning., , , , , , , , , and 30 other author(s). CoRR, (2020)Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction., , , , and . CoRR, (2024)Instability in clinical risk stratification models using deep learning., , , , , , , , , and 2 other author(s). ML4H@NeurIPS, volume 193 of Proceedings of Machine Learning Research, page 552-565. PMLR, (2022)Concept-based model explanations for electronic health records., , , , , , , , , and . CHIL, page 36-46. ACM, (2021)Walking with PACE - Personalized and Automated Coaching Engine., , , , , , , , , and 4 other author(s). UMAP, page 57-68. ACM, (2022)Underspecification Presents Challenges for Credibility in Modern Machine Learning., , , , , , , , , and 30 other author(s). J. Mach. Learn. Res., (2022)Automated LOINC Standardization Using Pre-trained Large Language Models., , , , , , , and . ML4H@NeurIPS, volume 193 of Proceedings of Machine Learning Research, page 343-355. PMLR, (2022)Boosting the interpretability of clinical risk scores with intervention predictions., , , , , , , , , and 1 other author(s). CoRR, (2022)Learning to Select Best Forecast Tasks for Clinical Outcome Prediction., , , , and . NeurIPS, (2020)Concept-based model explanations for Electronic Health Records., , , , , , , , , and . CoRR, (2020)