Natural Language Processing (NLP) is a study of automated processing of text data. Application of NLP in the clinical domain is important due to the rich unstructured information implanted in clinical documents, which often remains inaccessible in structured data. Empowered by the recent advance of language models (LMs), there is a growing interest in their application within the clinical domain. When applying NLP methods to a certain domain, the role of benchmark datasets are crucial as benchmark datasets not only guide the selection of best-performing models but also enable assessing of the reliability of the generated outputs. Despite the recent availability of LMs capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent. To address this issue, we propose LCD benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of MIMIC-IV and statewide death data. Our notes have a median word count of 1687 and an interquartile range of 1308 to 2169. We evaluated this benchmark dataset using baseline models, from bag-of-words and CNN to Hierarchical Transformer and an open-source instruction-tuned large language model. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations. We expect LCD benchmarks to become a resource for the development of advanced supervised models, prompting methods, or the foundation models themselves, tailored for clinical text. The benchmark dataset is available at https://github.com/Machine-Learning-for-Medical-Language/long-clinical-doc

The authors have declared no competing interest.

Research reported in this publication was supported by the National Library Of Medicine of the National Institutes of Health under Award Number R01LM012973, and by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH126977.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of Boston Children's Hospital gave ethical approval for this work (IRB number:IRB-P00028617). Under PhysioNet Credentialed Health Data Use Agreement 1.5.0 - Data Use Agreement for the MIMIC-IV (v2.2) and - Data Use Agreement for the MIMIC-IV-Note: Deidentified free-text clinical notes (v2.2) All authors are granted access to the database.

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The data underlying this article are available in a github repository, at https://github.com/Machine-Learning-for-Medical-Language/long-clinical-doc The datasets were derived from sources: https://physionet.org/content/mimiciv/2.2/ and https://physionet.org/content/mimic-iv-note/2.2/

https://github.com/Machine-Learning-for-Medical-Language/long-clinical-doc