Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases

Xianghao Zhan; Marie Humbert-Droz; Pritam Mukherjee; Olivier Gevaert

doi:10.1016/j.patter.2021.100289

Patterns (Jul 2021)

Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases

Xianghao Zhan,
Marie Humbert-Droz,
Pritam Mukherjee,
Olivier Gevaert

Affiliations

Xianghao Zhan: Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
Marie Humbert-Droz: Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA 94305, USA
Pritam Mukherjee: Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA 94305, USA
Olivier Gevaert: Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Corresponding author

DOI: https://doi.org/10.1016/j.patter.2021.100289
Journal volume & issue: Vol. 2, no. 7
p. 100289

Abstract

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Summary: Free-text clinical notes in electronic health records are more difficult for data mining while the structured diagnostic codes can be missing or erroneous. To improve the quality of diagnostic codes, this work extracts diagnostic codes from free-text notes: five old and new word vectorization methods were used to vectorize Stanford progress notes and predict eight ICD-10 codes of common cardiovascular diseases with logistic regression. The models showed good performance, with TF-IDF as the best vectorization model showing the highest AUROC (0.9499–0.9915) and AUPRC (0.2956–0.8072). The models also showed transferability when tested on MIMIC-III data with AUROC from 0.7952 to 0.9790 and AUPRC from 0.2353 to 0.8084. Model interpretability was shown by the important words with clinical meanings matching each disease. This study shows the feasibility of accurately extracting structured diagnostic codes, imputing missing codes, and correcting erroneous codes from free-text clinical notes for information retrieval and downstream machine-learning applications. The bigger picture: The mining of the structured data in electronic health records such as diagnostic codes enables many clinical applications, but much clinical information is locked in the unstructured free-text clinical notes because they are more difficult to use in data mining. In addition, the structured diagnostic codes are often missing or even erroneous. To accurately structure the free-text notes in the form of diagnostic code for downstream usage, we used old and new natural language processing methods together with interpretable classification algorithms to extract eight diagnostic codes of common cardiovascular diseases. This work helps to structure free-text clinical notes, impute missing diagnostic codes, and correct erroneously diagnostic codes noted by clinicians to improve the data quality of diagnostic codes as the fundamental structured data for later information retrieval and downstream data-mining applications.

Published in Patterns

ISSN: 2666-3899 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
Website: https://www.cell.com/patterns

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