Accuracy of using natural language processing methods for identifying healthcare-associated infections

Autor: Stéfan Jacques Darmoni, Frédérique Segond, Nastassia Tvardik, Ivan Kergourlay, M.-H. Metzger, André Bittar
Přispěvatelé: Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU), Equipe Traitement de l'information en Biologie Santé (TIBS - LITIS), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Xerox Research Centre Europe [Meylan], Xerox Company, ANR-12-TECS-0006,SYNODOS,SYstème de Normalisation et d'Organisation de Données médicales textuelles pour l'Observation en Santé(2012)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Healthcare associated infections
Adult
medicine.medical_specialty
animal structures
020205 medical informatics
Epidemiology
computerized
Specialty
Nice
Health Informatics
02 engineering and technology
Decision support systems
Healthcare-associated infections
computer.software_genre
Sensitivity and Specificity
Hospitals
University
03 medical and health sciences
Clinical
0302 clinical medicine
Intensive care
0202 electrical engineering
electronic engineering
information engineering
medicine
Electronic Health Records
Humans
[INFO]Computer Science [cs]
030212 general & internal medicine
computer.programming_language
Cross Infection
business.industry
Digestive surgery
Medical record
Natural language processing
Medical records systems
virus diseases
University hospital
3. Good health
Intensive Care Units
Artificial intelligence
business
computer
Algorithms
Zdroj: International Journal of Medical Informatics
International Journal of Medical Informatics, Elsevier, 2018, 117, pp.96-102. ⟨10.1016/j.ijmedinf.2018.06.002⟩
ISSN: 1386-5056
Popis: Objective There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medical records for epidemiological purposes. The objective of this study was to evaluate the performance of the SYNODOS data processing chain for detecting HAIs in clinical documents. Materials and methods The collection of textual records in these hospitals was carried out between October 2009 and December 2010 in three French University hospitals (Lyon, Rouen and Nice). The following medical specialties were included in the study: digestive surgery, neurosurgery, orthopedic surgery, adult intensive-care units. Reference Standard surveillance was compared with the results of automatic detection using NLP. Sensitivity on 56 HAI cases and specificity on 57 non-HAI cases were calculated. Results The accuracy rate was 84% (n = 95/113). The overall sensitivity of automatic detection of HAIs was 83.9% (CI 95%: 71.7–92.4) and the specificity was 84.2% (CI 95%: 72.1–92.5). The sensitivity varies from one specialty to the other, from 69.2% (CI 95%: 38.6–90.9) for intensive care to 93.3% (CI 95%: 68.1–99.8) for orthopedic surgery. The manual review of classification errors showed that the most frequent cause was an inaccurate temporal labeling of medical events, which is an important factor for HAI detection. Conclusion This study confirmed the feasibility of using NLP for the HAI detection in hospital facilities. Automatic HAI detection algorithms could offer better surveillance standardization for hospital comparisons.
Databáze: OpenAIRE
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