Title |
Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert
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Published in |
BMC Medical Informatics and Decision Making, October 2015
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DOI | 10.1186/s12911-015-0201-3 |
Pubmed ID | |
Authors |
Jayden MacRae, Tom Love, Michael G. Baker, Anthony Dowell, Matthew Carnachan, Maria Stubbe, Lynn McBain |
Abstract |
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. 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. 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. 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 33% |
India | 1 | 17% |
Argentina | 1 | 17% |
New Zealand | 1 | 17% |
Unknown | 1 | 17% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 50% |
Practitioners (doctors, other healthcare professionals) | 2 | 33% |
Scientists | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 3% |
Switzerland | 1 | 3% |
Unknown | 33 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 10 | 29% |
Researcher | 7 | 20% |
Student > Ph. D. Student | 4 | 11% |
Librarian | 3 | 9% |
Lecturer | 2 | 6% |
Other | 2 | 6% |
Unknown | 7 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 10 | 29% |
Computer Science | 6 | 17% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 6% |
Agricultural and Biological Sciences | 2 | 6% |
Veterinary Science and Veterinary Medicine | 1 | 3% |
Other | 6 | 17% |
Unknown | 8 | 23% |