NDLI: Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert

Content Provider	Springer Nature : BioMed Central
Author	MacRae, Jayden Love, Tom Baker, Michael G. Dowell, Anthony Carnachan, Matthew Stubbe, Maria McBain, Lynn
Abstract	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.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-015-0201-3.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-015-0201-3
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	15
Language	English
Publisher	BioMed Central
Publisher Date	2015-10-06
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Influenza Regular Expression Influenza Like Illness Problem List Read Code
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert

Automation of a problem list using natural language processing

Assessing the accuracy of an inter-institutional automated patient-specific health problem list

Discovering associations between problem list and practice setting

HERALD (Health Economics using Routine Anonymised Linked Data)

Fine-grained information extraction from German transthoracic echocardiography reports

Towards computerizing intensive care sedation guidelines: design of a rule-based architecture for automated execution of clinical guidelines

in silico Surveillance: evaluating outbreak detection with simulation models

Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality

Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert

Similar Documents

Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert

Automation of a problem list using natural language processing

Assessing the accuracy of an inter-institutional automated patient-specific health problem list

Discovering associations between problem list and practice setting

HERALD (Health Economics using Routine Anonymised Linked Data)

Fine-grained information extraction from German transthoracic echocardiography reports

Towards computerizing intensive care sedation guidelines: design of a rule-based architecture for automated execution of clinical guidelines

in silico Surveillance: evaluating outbreak detection with simulation models

Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality

Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert