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arXiv · 2006.07332

Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset

Thomas Searle·Zina Ibrahim·Richard JB Dobson
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Abstract

Clinical coding is currently a labour-intensive, error-prone, but 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 state of the art results. A popular dataset used in this task is MIMIC-III, a large intensive care database that includes clinical free text notes and associated codes. We argue for the reconsideration of the validity MIMIC-III's assigned codes that are often treated as gold-standard, especially when MIMIC-III has not undergone secondary validation. This work presents an open-source, reproducible experimental methodology for assessing the validity of codes derived from EHR discharge summaries. We exemplify the methodology with MIMIC-III discharge summaries and show the most frequently assigned codes in MIMIC-III are under-coded up to 35%.

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Thomas Searle, Zina Ibrahim, Richard JB Dobson. 2020-06-12. Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset. https://doi.org/10.18653/v1/2020.bionlp-1.8

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