ArticleBMC medical informatics and decision making2015
Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Fine-Tuning Large Language Models for Structured Extraction of Infectious Disease-Related Information From Clinical Notes in Japanese Primary Care: Development and Internal Validation Study.JMIR formative research · 2026Article
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- The identification of primary care consultation visits for otitis media: development of a software algorithm screening tool.Primary health care research & development · 2026Article
- Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.Journal of medical Internet research · 2021Article
- Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting.BMC public health · 2021Article
- Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore.JMIR medical informatics · 2018Article
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- Electronic Sentinel Surveillance of Influenza-like Illness. Experience from a pilot study in New Zealand.Applied clinical informatics · 2017Article
- Validation of a Syndromic Case Definition for Detecting Emergency Department Visits Potentially Related to Marijuana.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe designed and validated a rule-based expert system to identify influenza like illness (ILI) from routinely recorded general practice clinical narrative to aid a larger retrospective research study into the impact of the 2009 influenza pandemic in New Zealand.
methodsRules were assessed using pattern matching heuristics on routine clinical narrative. The system was trained using data from 623 clinical encounters and validated using a clinical expert as a gold standard against a mutually exclusive set of 901 records.
resultsWe calculated a 98.2 % specificity and 90.2 % sensitivity across an ILI incidence of 12.4 % measured against clinical expert classification. Peak problem list identification of ILI by clinical coding in any month was 9.2 % of all detected ILI presentations. Our system addressed an unusual problem domain for clinical narrative classification; using notational, unstructured, clinician entered information in a community care setting. It performed well compared with other approaches and domains. It has potential applications in real-time surveillance of disease, and in assisted problem list coding for clinicians.
conclusionsOur system identified ILI presentation with sufficient accuracy for use at a population level in the wider research study. The peak coding of 9.2 % illustrated the need for automated coding of unstructured narrative in our study.
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