ArticlePloS one2019
Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Customizing and fine-tuning opioid overdose machine learning models for community-specific dashboards.Journal of big data · 2026Article
- Improving drug identification in overdose death surveillance by using clinical natural language processing models.Journal of forensic sciences · 2026Article
- Modern sources of controls in case-control studies.American journal of epidemiology · 2025Article
- Precision public health in the era of genomics and big data.Nature medicine · 2024Review
- Innovations in public health surveillance: An overview of novel use of data and analytic methods.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- Question-answering system extracts information on injection drug use from clinical notes.Communications medicine · 2024Article
- An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records.PloS one · 2024Article
- Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study.JMIR public health and surveillance · 2023Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- Assessing and Addressing the Determinants of Appalachian Population Health: A Scoping Review.Journal of Appalachian health · 2023Article
- Priorities for successful use of artificial intelligence by public health organizations: a literature review.BMC public health · 2022Review
- Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records.Open forum infectious diseases · 2022Article
- Development and Validation of Machine Models Using Natural Language Processing to Classify Substances Involved in Overdose Deaths.JAMA network open · 2022Article
- Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial.Addiction (Abingdon, England) · 2022Article
- Using Machine Learning for Pharmacovigilance: A Systematic Review.Pharmaceutics · 2022Review
- Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundTimely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.
methodsUsing 2017-2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created as well as features indicating the part-of-speech of each word. These features were then used to train machine learning classifiers on 2017 data. The resulting models were tested on 2018 Kentucky data and compared to a simple rule-based classification approach. Documented code for this method is available for reuse and extensions: https://github.com/pjward5656/dcnlp.
resultsThe top scoring machine learning model achieved 0.96 positive predictive value (PPV) and 0.98 sensitivity for an F-score of 0.97 in identification of fatal drug overdoses on test data. This machine learning model achieved significantly higher performance for sensitivity (p<0.001) than the rule-based approach. Additional feature engineering may improve the model's prediction. This model can be deployed on death certificates as soon as the free-text is available, eliminating the time needed to code the death certificates.
conclusionMachine learning using natural language processing is a relatively new approach in the context of surveillance of health conditions. This method presents an accessible application of machine learning that improves the timeliness of drug overdose mortality surveillance. As such, it can be employed to inform public health responses to the drug overdose epidemic in near-real time as opposed to several weeks following events.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.