Identifying health information technology related safety event reports from patient safety event report databases

Journal of Biomedical Informatics
Allan FongRaj R Ratwani

Abstract

The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports. 5287 PSE reports manually coded as likely or unlikely related to HIT were used to train unigram, bigram, and combined unigram-bigram logistic regression and support vector machine models using five-fold cross validation. A difference-based scoring approach was used to prioritize and select unigram and bigram features by their relative importance to likely and unlikely HIT reports. A held-out set of 2000 manually coded reports were used for testing. Unigram models tended to perform better than bigram and combined models. A 300-unigram logistic regression had comparable classification performance to a 4030-unigram SVM model but with a faster relative run-time. The 300-unigram logistic regression model evaluated with the testing data had an AUC of 0.931 and a F1-score of 0.765. A difference-based scoring, prioritization, and feature selection approach can be used to generate simplified models with ...Continue Reading

Citations

Jul 25, 2020·JMIR Medical Informatics·Avishek Choudhury, Onur Asan
Sep 12, 2020·Journal of Biomedical Informatics·Hong Kang, Yang Gong
Jun 20, 2020·Journal of Patient Safety·Allan FongRaj M Ratwani
Nov 1, 2021·Methods of Information in Medicine·Asa AdadeyLorraine B Possanza

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