 Health system-scale language models are all-purpose prediction enginesLavender Yao Jiang,
Xujin Chris Liu,
Nima Pour Nejatian,
Mustafa Nasir-Moin,
Duo Wang,
Anas Abidin,
Kevin Eaton,
Howard Antony Riina,
Ilya Laufer,
Paawan Punjabi,
Madeline Miceli,
Nora C. Kim,
Cordelia Orillac,
Zane Schnurman,
Christopher Livia,
Hannah Weiss,
David Kurland,
Sean Neifert,
Yosef Dastagirzada,
Douglas Kondziolka,
Alexander T. M. Cheung,
Grace Yang,
Ming Cao,
Mona Flores,
Anthony B. Costa,
Yindalon Aphinyanaphongs,
Kyunghyun Cho and
Eric Karl Oermann ()
Nature, 2023, vol. 619, issue 7969, 357-362 Abstract: Abstract Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1–3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7–94.9%, with an improvement of 5.36–14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:619:y:2023:i:7969:d:10.1038_s41586-023-06160-y Ordering information: This journal article can be ordered from DOI: 10.1038/s41586-023-06160-y Access Statistics for this article Nature is currently edited by Magdalena Skipper More articles in Nature from Nature |
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