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The performance of predictive models in clinical settings often degrades when deployed in new hospitals due to distribution shifts.
Language models are few-shot learners
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Additive logistic regression: a statistical view of boosting
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PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals
Goldberger, A. L., L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley (2000) · 2000
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Greedy function approximation: a gradient boosting machine
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KDIGO clinical practice guideline for acute kidney injury; section 2: AKI definition
Acute Kidney Injury Work Group (2012) · 2012
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MIMIC-III, a freely accessible critical care database
Johnson, A. E., T. J. Pollard, L. Shen, L.-w. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. Anthony Celi, and R. G. Mark (2016) · 2016
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Using transfer learning for improved mortality prediction in a data-scarce hospital setting
Desautels, T., J. Calvert, J. Hoffman, Q. Mao, M. Jay, G. Fletcher, C. Barton, U. Chettipally, Y. Kerem, and R. Das (2017) · 2017
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LightGBM: A highly efficient gradient boosting decision tree
Ke, G., Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu (2017) · 2017
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Deeper, broader and artier domain generalization
Li, D., Y. Yang, Y.-Z. Song, and T. M. Hospedales (2017) · 2017
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The eICU collaborative research database, a freely available multi-center database for critical care research
Pollard, T. J., A. E. Johnson, J. D. Raffa, L. A. Celi, R. G. Mark, and O. Badawi (2018) · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and T. Dietterich (2019) · 2019
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Paediatric intensive care database (version 1.1.0)
Li, H., X. Zeng, and G. Yu (2019) · 2019
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A clinically applicable approach to continuous prediction of future acute kidney injury
Tomašev, N., X. Glorot, J. W. Rae, M. Zielinski, H. Askham, A. Saraiva, A. Mottram, C. Meyer, S. Ravuri, I. Protsyuk, et al. (2019) · 2019
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Invariance, causality and robustness
Bühlmann, P. (2020) · 2020
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Early prediction of circulatory failure in the intensive care unit using machine learning
Hyland, S. L., M. Faltys, M. Hüser, X. Lyu, T. Gumbsch, C. Esteban, C. Bock, M. Horn, M. Moor, B. Rieck, et al. (2020) · 2020
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Distributionally robust neural networks
Sagawa, S., P. W. Koh, T. B. Hashimoto, and P. Liang (2020) · 2020
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Prediction across healthcare settings: a case study in predicting emergency department disposition
Barak-Corren, Y., P. Chaudhari, J. Perniciaro, M. Waltzman, A. M. Fine, and B. Y. Reis (2021) · 2021
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In search of lost domain generalization
Gulrajani, I. and D. Lopez-Paz (2021) · 2021
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Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine
Guo, L. L., S. R. Pfohl, J. Fries, J. Posada, S. L. Fleming, C. Aftandilian, N. Shah, and L. Sung (2021) · 2021
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MIMIC-IV, a freely accessible electronic health record dataset
Johnson, A. E., L. Bulgarelli, L. Shen, A. Gayles, A. Shammout, S. Horng, T. J. Pollard, S. Hao, B. Moody, B. Gow, et al. (2023) · 2023
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Predicting sepsis using deep learning across international sites: a retrospective development and validation study
Moor, M., N. Bennett, D. Plečko, M. Horn, B. Rieck, N. Meinshausen, P. Bühlmann, and K. Borgwardt (2023) · 2023
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Towards foundation models for critical care time series
Burger, M., F. Sergeev, M. Londschien, D. Chopard, H. Yèche, E. Gerdes, P. Leshetkina, A. Morgenroth, Z. Babür, J. Bogojeska, M. Faltys, R. Kuznetsova, and G. Rätsch (2024) · 2024
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A comprehensive ml-based respiratory monitoring system for physiological monitoring & resource planning in the ICU
Hüser, M., X. Lyu, M. Faltys, A. Pace, M. Hoche, S. Hyland, H. Yèche, M. Burger, T. M. Merz, and G. Rätsch (2024) · 2024
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Achievable distributional robustness when the robust risk is only partially identified
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Anchor regression: Heterogeneous data meet causality
Rothenhäusler, D., N. Meinshausen, P. Bühlmann, and J. Peters (2021) · 2021
Cited alongside, same era.
Sharing ICU patient data responsibly under the society of critical care medicine/European society of intensive care medicine joint data science collaboration: the Amsterdam university medical centers database (AmsterdamUMCdb) example
Thoral, P. J., J. M. Peppink, R. H. Driessen, E. J. Sijbrands, E. J. Kompanje, L. Kaplan, H. Bailey, J. Kesecioglu, M. Cecconi, M. Churpek, et al. (2021) · 2021
Cited alongside, same era.
HiRID-ICU-benchmark — a comprehensive machine learning benchmark on high-resolution ICU data
Yèche, H., R. Kuznetsova, M. Zimmermann, M. Hüser, X. Lyu, M. Faltys, and G. Rätsch (2021) · 2021
Cited alongside, same era.
A causal framework for distribution generalization
Christiansen, R., N. Pfister, M. E. Jakobsen, N. Gnecco, and J. Peters (2022) · 2022
Cited alongside, same era.
Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine
Guo, L. L., S. R. Pfohl, J. Fries, A. E. Johnson, J. Posada, C. Aftandilian, N. Shah, and L. Sung (2022) · 2022
Cited alongside, same era.
Distributional anchor regression
Kook, L., B. Sick, and P. Bühlmann (2022) · 2022
Cited alongside, same era.
Domain shifts in machine learning based Covid-19 diagnosis from blood tests
Roland, T., C. Böck, T. Tschoellitsch, A. Maletzky, S. Hochreiter, J. Meier, and G. Klambauer (2022) · 2022
Cited alongside, same era.
Kostin, J., N. Gnecco, and F. Yang (2024) · 2024
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Londschien, M. and P. Bühlmann (2024) · 2024
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An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit
Lyu, X., B. Fan, M. Hüser, P. Hartout, T. Gumbsch, M. Faltys, T. M. Merz, G. Rätsch, and K. Borgwardt (2024) · 2024
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Northwestern ICU (NWICU) database (version 0.1.0)
Moukheiber, D., W. Temps, B. Molgi, Y. Li, A. Lu, P. Nannapaneni, A. Chahin, S. Hao, F. Torres Fabregas, L. A. Celi, A. Wong, M. Lloyd, B. F. X., H. Lee, D. Schneider, T. Pollard, Y. Luo, A. Kho, and R. Mark (2024) · 2024
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The impact of multi-institution datasets on the generalizability of machine learning prediction models in the ICU
Rockenschaub, P., A. Hilbert, T. Kossen, P. Elbers, F. von Dincklage, V. Madai, and D. Frey (2024) · 2024
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Harnessing big data in critical care: Exploring a new european dataset
Rodemund, N., B. Wernly, C. Jung, C. Cozowicz, and A. Koköfer (2024) · 2024
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Yet another ICU benchmark: A flexible multi-center framework for clinical ML
van de Water, R., H. N. A. Schmidt, P. Elbers, P. Thoral, B. Arnrich, and P. Rockenschaub (2024) · 2024
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A foundation model for intensive care unlocking generalization across tasks and domains at scale
Burger, M., D. Chopard, M. Londschien, F. Sergeev, H. Yèche, R. Kuznetsova, M. Faltys, E. Gerdes, P. Leshetkina, P. Bühlmann, and G. Rätsch (2025) · 2025
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Causality-inspired robustness for nonlinear models via representation learning
Sola, M., P. Bühlmann, and X. Shen (2025) · 2025
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AnchorForest
Ulmer, M. and C. Scheidegger (2025) · 2025
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Spectrally deconfounded random forests
Ulmer, M., C. Scheidegger, and P. Bühlmann (2025) · 2025
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