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    <abstract>Clinical coding is currently a labour-intensive, error-prone, but a critical administrative process whereby hospital patient episodes are manually assigned codes by qualified staff from large, standardised taxonomic hierarchies of codes. Automating clinical coding has a long history in NLP research and has recently seen novel developments setting new benchmark results. A popular dataset used in this task is MIMIC-III, a large database of clinical free text notes and their associated codes amongst other data. We argue for the reconsideration of the validity MIMIC-III’s assigned codes, as MIMIC-III has not undergone secondary validation. This work presents an open-source, reproducible experimental methodology for assessing the validity of EHR discharge summaries. We exemplify the methodology with MIMIC-III discharge summaries and show the most frequently assigned codes in MIMIC-III are undercoded up to 35%.</abstract>
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%0 Conference Proceedings
%T Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset
%A Searle, Thomas
%A Ibrahim, Zina
%A Dobson, Richard
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F searle-etal-2020-experimental
%X Clinical coding is currently a labour-intensive, error-prone, but a critical administrative process whereby hospital patient episodes are manually assigned codes by qualified staff from large, standardised taxonomic hierarchies of codes. Automating clinical coding has a long history in NLP research and has recently seen novel developments setting new benchmark results. A popular dataset used in this task is MIMIC-III, a large database of clinical free text notes and their associated codes amongst other data. We argue for the reconsideration of the validity MIMIC-III’s assigned codes, as MIMIC-III has not undergone secondary validation. This work presents an open-source, reproducible experimental methodology for assessing the validity of EHR discharge summaries. We exemplify the methodology with MIMIC-III discharge summaries and show the most frequently assigned codes in MIMIC-III are undercoded up to 35%.
%R 10.18653/v1/2020.bionlp-1.8
%U https://aclanthology.org/2020.bionlp-1.8
%U https://doi.org/10.18653/v1/2020.bionlp-1.8
%P 76-85

Markdown (Informal)

[Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset](https://aclanthology.org/2020.bionlp-1.8) (Searle et al., BioNLP 2020)

• Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset (Searle et al., BioNLP 2020)

ACL

• Thomas Searle, Zina Ibrahim, and Richard Dobson. 2020. Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 76–85, Online. Association for Computational Linguistics.