Deep neural networks for fine-grained surveillance of overdose mortality
This work improves surveillance of drug overdose deaths by enabling more accurate and automated identification of substances, addressing a domain-specific need in public health.
The paper tackled the problem of identifying specific substances from free-text death certificates for drug overdose surveillance, achieving an F1-score of 99.13% with a deep learning named-entity recognition model.
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 a 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, 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.