In American journal of epidemiology ; h5-index 65.0
Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, 10th Revision (ICD-10) codes present on death certificates, however ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on death certificate, the free-text cause of death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certificates rely solely on look-up tables for identifying specific substances, which must be frequently updated and maintained. To improve identification of drugs on death certificates, a deep learning named-entity recognition model was developed, utilizing data from the Kentucky Drug Overdose Fatality Surveillance System, years 2014-2019, which achieved an F1-score of 99.13%. This model can identify new drug misspellings and novel substances that are not present on current surveillance look-up tables, enhancing the surveillance of drug overdose deaths.
Ward Patrick J, Young April M, Slavova Svetla, Liford Madison, Daniels Lara, Lucas Ripley, Kavuluru Ramakanth
deep learning, drug overdose, machine learning, surveillance