Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study.
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Eye Infections, Viral
/ diagnosis
Female
Follow-Up Studies
Herpes Zoster Ophthalmicus
/ diagnosis
Herpesvirus 3, Human
Humans
Male
Middle Aged
Natural Language Processing
Population Surveillance
/ methods
ROC Curve
Reproducibility of Results
Retrospective Studies
Young Adult
HZO
diagnosis code
electronic medical record
epidemiology
natural language processing
Journal
Clinical & experimental ophthalmology
ISSN: 1442-9071
Titre abrégé: Clin Exp Ophthalmol
Pays: Australia
ID NLM: 100896531
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
27
04
2018
accepted:
13
06
2018
pubmed:
20
6
2018
medline:
11
2
2020
entrez:
20
6
2018
Statut:
ppublish
Résumé
Diagnosis codes are inadequate for accurately identifying herpes zoster (HZ) ophthalmicus (HZO). There is significant lack of population-based studies on HZO due to the high expense of manual review of medical records. To assess whether HZO can be identified from the clinical notes using natural language processing (NLP). To investigate the epidemiology of HZO among HZ population based on the developed approach. A retrospective cohort analysis. A total of 49 914 southern California residents aged over 18 years, who had a new diagnosis of HZ. An NLP-based algorithm was developed and validated with the manually curated validation data set (n = 461). The algorithm was applied on over 1 million clinical notes associated with the study population. HZO versus non-HZO cases were compared by age, sex, race and co-morbidities. We measured the accuracy of NLP algorithm. NLP algorithm achieved 95.6% sensitivity and 99.3% specificity. Compared to the diagnosis codes, NLP identified significant more HZO cases among HZ population (13.9% vs. 1.7%). Compared to the non-HZO group, the HZO group was older, had more males, had more Whites and had more outpatient visits. We developed and validated an automatic method to identify HZO cases with high accuracy. As one of the largest studies on HZO, our finding emphasizes the importance of preventing HZ in the elderly population. This method can be a valuable tool to support population-based studies and clinical care of HZO in the era of big data.
Sections du résumé
IMPORTANCE
Diagnosis codes are inadequate for accurately identifying herpes zoster (HZ) ophthalmicus (HZO). There is significant lack of population-based studies on HZO due to the high expense of manual review of medical records.
BACKGROUND
To assess whether HZO can be identified from the clinical notes using natural language processing (NLP). To investigate the epidemiology of HZO among HZ population based on the developed approach.
DESIGN
A retrospective cohort analysis.
PARTICIPANTS
A total of 49 914 southern California residents aged over 18 years, who had a new diagnosis of HZ.
METHODS
An NLP-based algorithm was developed and validated with the manually curated validation data set (n = 461). The algorithm was applied on over 1 million clinical notes associated with the study population. HZO versus non-HZO cases were compared by age, sex, race and co-morbidities.
MAIN OUTCOME MEASURES
We measured the accuracy of NLP algorithm.
RESULTS
NLP algorithm achieved 95.6% sensitivity and 99.3% specificity. Compared to the diagnosis codes, NLP identified significant more HZO cases among HZ population (13.9% vs. 1.7%). Compared to the non-HZO group, the HZO group was older, had more males, had more Whites and had more outpatient visits.
CONCLUSIONS AND RELEVANCE
We developed and validated an automatic method to identify HZO cases with high accuracy. As one of the largest studies on HZO, our finding emphasizes the importance of preventing HZ in the elderly population. This method can be a valuable tool to support population-based studies and clinical care of HZO in the era of big data.
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
7-14Informations de copyright
© 2018 Royal Australian and New Zealand College of Ophthalmologists.