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        <title>Section Classification in Clinical Notes with Multi-task Transformers</title>
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    <abstract>Clinical notes are the backbone of electronic health records, often containing vital information not observed in other structured data. Unfortunately, the unstructured nature of clinical notes can lead to critical patient-related information being lost. Algorithms that organize clinical notes into distinct sections are often proposed in order to allow medical professionals to better access information in a given note. These algorithms, however, often assume a given partition over the note, and classify section types given this information. In this paper, we propose a multi-task solution for note sectioning, where a single model identifies context changes and labels each section with its medically-relevant title. Results on in-distribution (MIMIC-III) and out-of-distribution (private held-out) datasets reveal that our approach successfully identifies note sections across different hospital systems.</abstract>
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%0 Conference Proceedings
%T Section Classification in Clinical Notes with Multi-task Transformers
%A Zhang, Fan
%A Laish, Itay
%A Benjamini, Ayelet
%A Feder, Amir
%S Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F zhang-etal-2022-section
%X Clinical notes are the backbone of electronic health records, often containing vital information not observed in other structured data. Unfortunately, the unstructured nature of clinical notes can lead to critical patient-related information being lost. Algorithms that organize clinical notes into distinct sections are often proposed in order to allow medical professionals to better access information in a given note. These algorithms, however, often assume a given partition over the note, and classify section types given this information. In this paper, we propose a multi-task solution for note sectioning, where a single model identifies context changes and labels each section with its medically-relevant title. Results on in-distribution (MIMIC-III) and out-of-distribution (private held-out) datasets reveal that our approach successfully identifies note sections across different hospital systems.
%R 10.18653/v1/2022.louhi-1.7
%U https://aclanthology.org/2022.louhi-1.7
%U https://doi.org/10.18653/v1/2022.louhi-1.7
%P 54-59

Markdown (Informal)

[Section Classification in Clinical Notes with Multi-task Transformers](https://aclanthology.org/2022.louhi-1.7) (Zhang et al., Louhi 2022)

• Section Classification in Clinical Notes with Multi-task Transformers (Zhang et al., Louhi 2022)

ACL

• Fan Zhang, Itay Laish, Ayelet Benjamini, and Amir Feder. 2022. Section Classification in Clinical Notes with Multi-task Transformers. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 54–59, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.