ArticlePloS one2020
Machine learning for syndromic surveillance using veterinary necropsy reports.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- Development and implementation of a data parsing protocol for companion animal cancer data.Veterinary pathology · 2026Article
- Development of criteria to optimize manual smear review of automated complete blood counts using a machine learning model.Veterinary clinical pathology · 2025Article
- Machine-learning prediction of postoperative complications after high tibial osteotomy for canine cranial cruciate ligament disease.Veterinary surgery : VS · 2025Article
- Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.Veterinary surgery : VS · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility toAnimals : an open access journal from MDPI · 2023Article
- Association between case signalment and disease diagnosis in urinary bladder disease in Australian cats and dogs.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2021Article
- Research perspectives on animal health in the era of artificial intelligence.Veterinary research · 2021Review
- Day-1 Competencies for Veterinarians Specific to Health Informatics.Frontiers in veterinary science · 2021Article
- Autoregressive Models Applied to Time-Series Data in Veterinary Science.Frontiers in veterinary science · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The use of natural language data for animal population surveillance represents a valuable opportunity to gather information about potential disease outbreaks, emerging zoonotic diseases, or bioterrorism threats. In this study, we evaluate machine learning methods for conducting syndromic surveillance using free-text veterinary necropsy reports. We train a system to detect if a necropsy report from the Wisconsin Veterinary Diagnostic Laboratory contains evidence of gastrointestinal, respiratory, or urinary pathology. We evaluate the performance of several machine learning algorithms including deep learning with a long short-term memory network. Although no single algorithm was superior, random forest using feature vectors of TF-IDF statistics ranked among the top-performing models with F1 scores of 0.923 (gastrointestinal), 0.960 (respiratory), and 0.888 (urinary). This model was applied to over 33,000 necropsy reports and was used to describe temporal and spatial features of diseases within a 14-year period, exposing epidemiological trends and detecting a potential focus of gastrointestinal disease from a single submitting producer in the fall of 2016.
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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.