TY - JOUR
AU1 - Rozova, Vlada
AU2 - Witt, Katrina
AU3 - Robinson, Jo
AU4 - Li, Yan
AU5 - Verspoor, Karin
AB - ObjectiveAccurate identification of self-harm presentations to Emergency Departments (ED) can lead to more timely mental health support, aid in understanding the burden of suicidal intent in a population, and support impact evaluation of public health initiatives related to suicide prevention. Given lack of manual self-harm reporting in ED, we aim to develop an automated system for the detection of self-harm presentations directly from ED triage notes.Materials and methodsWe frame this as supervised classification using natural language processing (NLP), utilizing a large data set of 477 627 free-text triage notes from ED presentations in 2012–2018 to The Royal Melbourne Hospital, Australia. The data were highly imbalanced, with only 1.4% of triage notes relating to self-harm. We explored various preprocessing techniques, including spelling correction, negation detection, bigram replacement, and clinical concept recognition, and several machine learning methods.ResultsOur results show that machine learning methods dramatically outperform keyword-based methods. We achieved the best results with a calibrated Gradient Boosting model, showing 90% Precision and 90% Recall (PR-AUC 0.87) on blind test data. Prospective validation of the model achieves similar results (88% Precision; 89% Recall).DiscussionED notes are noisy texts, and simple token-based models work best. Negation detection and concept recognition did not change the results while bigram replacement significantly impaired model performance.ConclusionThis first NLP-based classifier for self-harm in ED notes has practical value for identifying patients who would benefit from mental health follow-up in ED, and for supporting surveillance of self-harm and suicide prevention efforts in the population.
TI - Detection of self-harm and suicidal ideation in emergency department triage notes
JF - Journal of the American Medical Informatics Association
DO - 10.1093/jamia/ocab261
DA - 2021-12-13
UR - https://www.deepdyve.com/lp/oxford-university-press/detection-of-self-harm-and-suicidal-ideation-in-emergency-department-5lWK2AmWoq
SP - 472
EP - 480
VL - 29
IS - 3
DP - DeepDyve
ER -