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Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert
BACKGROUND: We 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. METHODS: Rules were assess...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4596422/ https://www.ncbi.nlm.nih.gov/pubmed/26445235 http://dx.doi.org/10.1186/s12911-015-0201-3 |
Sumario: | BACKGROUND: We 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. METHODS: Rules 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. RESULTS: We 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. CONCLUSIONS: Our 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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