ArticleJournal of biomedical informatics2015
A method for detecting and characterizing outbreaks of infectious disease from clinical reports.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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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.
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Who cites it
12 citing papers in PubMed.
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- The quality of vital signs measurements and value preferences in electronic medical records varies by hospital, specialty, and patient demographics.Scientific reports · 2023Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription.Journal of biomedical informatics · 2021Article
- Article
- The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza.Online journal of public health informatics · 2019Article
- Article
- A Bayesian system to detect and characterize overlapping outbreaks.Journal of biomedical informatics · 2017Article
- Public Health, Population Health, and Epidemiology Informatics: Recent Research and Trends in the United States.Yearbook of medical informatics · 2017Review
- The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.Applied clinical informatics · 2017Article
- A study of the transferability of influenza case detection systems between two large healthcare systems.PloS one · 2017Article
- Some Innovative Approaches for Public Health and Epidemiology Informatics.Yearbook of medical informatics · 2016Article
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
Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.
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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.