Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation
| Autor: | Vanessa W. Stevens, Kelly S. Peterson, Olga V. Patterson, Alec B. Chapman, Makoto Jones, Katherine S. Wallace, Gary A. Roselle, Julia Lewis, Patricia A Lye, Daniel W. Denhalter, Shantini D. Gamage |
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| Rok vydání: | 2021 |
| Předmět: |
biosurveillance
Male medicine.medical_specialty 020205 medical informatics Computer science Information Storage and Retrieval Health Informatics 02 engineering and technology infectious disease surveillance Communicable Diseases Emerging surveillance applications Machine Learning 03 medical and health sciences Zika 0302 clinical medicine Documentation Cohen's kappa Text processing Health care 0202 electrical engineering electronic engineering information engineering medicine Electronic Health Records Humans Public Health Surveillance 030212 general & internal medicine natural language processing Original Paper Travel business.industry Public health Public Health Environmental and Occupational Health COVID-19 Reproducibility of Results electronic health record Middle Aged Data science United States Infectious disease (medical specialty) travel history Preparedness Feasibility Studies Female Language model Public aspects of medicine RA1-1270 business Algorithms |
| Zdroj: | JMIR Public Health and Surveillance JMIR Public Health and Surveillance, Vol 7, Iss 3, p e26719 (2021) |
| ISSN: | 2369-2960 |
| DOI: | 10.2196/26719 |
| Popis: | BackgroundPatient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.ObjectiveThis study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.MethodsClinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.ResultsAmong 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.ConclusionsAutomated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases. |
| Databáze: | OpenAIRE |
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