Evidence map›Paper›PMID 41656761›Full record

ArticleJournal of forensic sciences2026

Improving drug identification in overdose death surveillance by using clinical natural language processing models.

Arthur J Funnell, Panayiotis Petousis, Fabrice Harel-Canada, Ruby Romero, Alex A T Bui, Adam Koncsol, Hritika Chaturvedi, Chelsea Shover, David Goodman-Meza

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of forensic sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Arthur J FunnellMedical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Panayiotis PetousisMedical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Fabrice Harel-CanadaComputer Science Department, University of California, Los Angeles, Los Angeles, California, USA.
Ruby RomeroComputational and Systems Biology, University of California, Los Angeles, Los Angeles, California, USA.
Alex A T BuiMedical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Adam KoncsolDivision of General Internal Medicine and Health Services Research, University of California, Los Angeles, Los Angeles, California, USA.
Hritika ChaturvediComputational and Systems Biology, University of California, Los Angeles, Los Angeles, California, USA.
Chelsea ShoverDivision of General Internal Medicine and Health Services Research, University of California, Los Angeles, Los Angeles, California, USA.
David Goodman-MezaKirby Institute, University of New South Wales, Sydney, New South Wales, Australia.

Funding

Predicting fatal and non-fatal overdose in Los Angeles County with Rapid Overdose Surveillance Dashboard to target street-based addiction treatment and harm reduction servicesR01DA057630 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI David Goodman, Chelsea Leigh Shover · 2022 to 2026
$2.9M
Adoption and scale-up of long-acting medications for opioid use disorder by U.S. clinicians.K01DA050771 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SHOVER, CHELSEA LEIGH · 2021 to 2025
$893k
NIDA NIH HHS K01 DA050771NIDA NIH HHS K01-DA050771NIDA NIH HHS R01 DA057630NIDA NIH HHS R01-DA57630
6 · The paper itself

Abstract

The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical overdose data are often buried in free-text coroner reports, leading to delays and information loss when coded into ICD (International Classification of Disease)-10 classifications. Natural language processing (NLP) models may automate and enhance overdose surveillance, but prior applications have been limited. A dataset of 35,433 death records from multiple US jurisdictions in 2020 was used for model training and internal testing. External validation was conducted using a novel separate dataset of 3335 records from 2023 to 2024. Multiple NLP approaches were evaluated for classifying specific drug involvement from unstructured death certificate text. These included traditional single- and multi-label classifiers, as well as fine-tuned encoder-only language models such as Bidirectional Encoder Representations from Transformers (BERT) and BioClinicalBERT, and contemporary decoder-only large language models (LLMs) such as Qwen 3 and Llama 3. Model performance was assessed using macro-averaged F

Indexed as

Death CertificatesDrug OverdoseNatural Language ProcessingHumansLarge Language ModelsUnited StatesBidirectional Encoder Representations from Transformers (BERT)BioClinicalBERTdeath certificate analysisdrug‐related death classificationforensic toxicologylarge language modelsmulti‐label classificationnatural language processingopioids toxicityoverdose surveillancepolysubstance detection

Identifiers

PMID41656761
PMCPMC13139796

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.