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

ABSTRACT: 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 meanin

SUBMITTER: Zhan X 

PROVIDER: S-EPMC8276012 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature