Bergquist, Timothy; Schaffter, Thomas; Yan, Yao; Yu, Thomas; Prosser, Justin; Gao, Jifan; Chen, Guanhua; Charzewski, Łukasz; Nawalany, Zofia; Brugere, Ivan; Retkute, Renata; Prusokas, Alidivinas; Prusokas, Augustinas; Choi, Yonghwa; Lee, Sanghoon; Choe, Junseok; Lee, Inggeol; Kim, Sunkyu; Kang, Jaewoo; Mooney, Sean; Guinney, Justin; Consortium, Patient (2023)
Bergquist, Timothy; Schaffter, Thomas; Yan, Yao; Yu, Thomas; Prosser, Justin...
Journal of the American Medical Informatics Association: JAMIA 31 (1), 35–44.
DOI: 10.1093/jamia/ocad159
OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.
Thamm, M.; Reiß, Fabienne; Sohl, Leon; Gabel, M.; Noll, Matthias; Scheiner, Ricarda (2023)
MDPI microorganisms 11, 2780.
DOI: 10.3390/microorganisms11112780
Weinmann, Natalie; Prent, Lilian (2023)
Holtorf, Christian (2023)
Ringvorlesung "Interdisziplinarität in Studium und Beruf", Hochschule Konstanz..
Kohls, Niko (2023)
Kohls, Niko (2023)
Schaub, Michael (2023)
Vortragsreihe des Klima- und Umweltbeirats der Gemeinde Dörfles-Esbach.
Kohls, Niko (2023)
Deloie, Dario; Kröger, Christine (2023)
Soziale Arbeit - Zeitschrift für soziale und sozialverwandte Gebiete 72. Jahrgang (11), 414-421.
Schaub, Michael (2023)
Themenabend VDI-Bezirksgruppe Coburg.
DOI: 10.13140/RG.2.2.22273.63849
Kohls, Niko (2023)
Zagel, Christian (2023)
DEHOGA Bayern.
Wilde, Mathias; Riedelbauch, Lukas (2023)
Nahverkehrs-praxis - Fachzeitschrift für moderne Mobilität (11/12), 20-23.
Rüdel, Thomas; Leidner, Jochen L. (2023)
Technical Report, ArXiv Pre-Print Server.
DOI: 10.48550/arXiv.2311.11701
Schaub, Michael (2023)
Transforming Economies.
Kohls, Niko; Leyva, Monica A.; Giordano, James (2023)
Placebo Effects Through the Lens of Translational Research 2023, 251 - 266.
DOI: 10.1093/med/9780197645444.003.0019
Heinrich, Michael (2023)
Vortrag und Diskussion, Schönheit im Wandel der Zeiten, Vorlesungsreihe, Lehrstuhl Philosophie II, Universität Bamberg, 2023.
Fritsche, Manuel; Epple, Philipp; Delgado , Antonio (2023)
ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023) New Orleans, Louisiana, USA October 29–November 2, 2023..
Fritsche, Manuel; Epple, Philipp; Delgado , Antonio (2023)
ASME 2023 International Mechanical Engineering Congress and Exposition (IMECE2023) New Orleans, Louisiana, USA October 29–November 2, 2023..
Panzardi, Enza; Drese, Klaus Stefan; Mugnaini, Marco; Parri, Lorenzo ; Vignoli, Valerio; Fort, Ada (2023)
Panzardi, Enza; Drese, Klaus Stefan; Mugnaini, Marco; Parri, Lorenzo ; Vignoli, Valerio...
IEEE Transactions on Instrumentation and Measurement 2023.
DOI: 10.1109/TIM.2023.3328691