ArticleJournal of hospital medicine2022
Derivation of a natural language processing algorithm to identify febrile infants.
Article in Journal of hospital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- A Population-Based Study of Rates of Invasive Bacterial Infection (IBI) and Missed IBI in Febrile Infants 8-90 Days of Age.The Journal of pediatrics · 2025Article
- Variability in Invasive Bacterial Infection Proportions Among Febrile Infants Aged 8-90 Days Using Administrative Data.Academic pediatrics · 2025Article
- Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.Hospital pediatrics · 2025Article
- A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning.BMC veterinary research · 2024Article
- Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.Journal of hospital medicine · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundDiagnostic codes can retrospectively identify samples of febrile infants, but sensitivity is low, resulting in many febrile infants eluding detection. To ensure study samples are representative, an improved approach is needed.
objectiveTo derive and internally validate a natural language processing algorithm to identify febrile infants and compare its performance to diagnostic codes.
methodsThis cross-sectional study consisted of infants aged 0-90 days brought to one pediatric emergency department from January 2016 to December 2017. We aimed to identify infants with fever, defined as a documented temperature ≥38°C. We used 2017 clinical notes to develop two rule-based algorithms to identify infants with fever and tested them on data from 2016. Using manual abstraction as the gold standard, we compared performance of the two rule-based algorithms (Models 1, 2) to four previously published diagnostic code groups (Models 5-8) using area under the receiver-operating characteristics curve (AUC), sensitivity, and specificity.
resultsFor the test set (n = 1190 infants), 184 infants were febrile (15.5%). The AUCs (0.92-0.95) and sensitivities (86%-92%) of Models 1 and 2 were significantly greater than Models 5-8 (0.67-0.74; 20%-74%) with similar specificities (93%-99%). In contrast to Models 5-8, samples from Models 1 and 2 demonstrated similar characteristics to the gold standard, including fever prevalence, median age, and rates of bacterial infections, hospitalizations, and severe outcomes.
conclusionsFindings suggest rule-based algorithms can accurately identify febrile infants with greater sensitivity while preserving specificity compared to diagnostic codes. If externally validated, rule-based algorithms may be important tools to create representative study samples, thereby improving generalizability of findings.
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