Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach

Abstract

Background Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. Objective This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. Methods The study includes 283,468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40,000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. Results The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. Conclusions This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals’ efforts in manually classifying diagnoses for research purposes.

Use of a Large Language Model to Identify and Classify Injuries With Free-Text Emergency Department Data

“…The study analyzed 283 468 medical records of the pediatric ED of Padova University Hospital in Padova, Italy, from January 1, 2007, to December 31, 2018. 3 The Azienda Ospedaliera di Padova Ethics Committee approved this cross-sectional study. Patients signed a written consent form to allow the use of data for scientific purposes.…”

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

“…Motivated by the establishment of various diagnostic signatures based on REOs to aid clinical HCC diagnosis decision, we designed robust and powerful predictors in this work. The developed predictors hybridized several algorithms, i.e., REOs, mRMR 21 , MRMD 22 , support vector machine (SVM) 23 , 24 , k-nearest neighbor (KNN) 24 , decision tree (DT) 25 , 26 , logistic regression (LR) 26 , extreme gradient boosting (XGBoost) 24 , logistic model trees (LMT) 27 , adaptive boosting M1 (AdaBoostM1) 28 and naïve bayes (NB) 29 . The REOs method was used for feature construction, mRMR and MRMD were used for feature ranking and selection, 2902 secreted genes (genes encoding secreted proteins) collected public database were used for feature filtering, and SVM, KNN, DT, LR, XGBoost, LMT, AdaBoostM1 and NB algorithms were used for classification purposes.…”