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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort

Andrew J McMurry1,2, Amy R Zipursky1,3, Alon Geva1,4,5

  • 1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.

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|April 4, 2024
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Summary
This summary is machine-generated.

Artificial intelligence (AI) natural language processing (NLP) accurately detects COVID-19 symptoms in pediatric emergency department patients, outperforming ICD-10 codes. This technology is crucial for dynamic infectious disease surveillance and understanding symptom evolution.

Keywords:
AICOVID-19SARS-CoV-2adolescentadolescentsartificial intelligencechildchildrenclinical noteclinical notesdetectdetectiondiagnosediagnosisdiagnosticdiagnosticsdocumentationemergencyinfectiousnatural language processingpaediatricpaediatricspediatricpediatricspipelinepipelinespublic health, biosurveillancepulmonaryrespiratorysurveillancesymptomsymptomsteenteenagerteenagersteensurgentyouth

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Area of Science:

  • Infectious Disease Epidemiology
  • Computational Health Informatics
  • Pediatric Emergency Medicine

Background:

  • Real-time infectious disease surveillance requires adaptable case definitions, often relying on symptoms.
  • Electronic health records are key for extracting symptom data in population health monitoring and research.
  • Accurate, timely symptom detection is vital for public health response during outbreaks.

Purpose of the Study:

  • To validate an AI-powered natural language processing (NLP) pipeline for identifying COVID-19 symptoms in pediatric patients' clinical notes.
  • To assess the pipeline's performance in emergency department (ED) settings, utilizing pediatric patients as sentinel cases.
  • To compare NLP performance against International Classification of Diseases, 10th Revision (ICD-10) coding for symptom detection.

Main Methods:

  • Retrospective cohort study of 85,678 pediatric ED encounters (March 2020 - May 2022) at a children's hospital.
  • An NLP pipeline processed ED notes to detect 11 COVID-19 symptoms based on CDC criteria.
  • Performance metrics (F1-score, PPV, sensitivity, specificity) compared NLP and ICD-10 coding against expert-labeled gold standard data.

Main Results:

  • NLP demonstrated superior accuracy (F1-score=0.796) in detecting COVID-19 symptoms compared to ICD-10 codes (F1-score=0.451).
  • NLP showed higher sensitivity for positive symptoms (0.930 vs. 0.300), while ICD-10 had higher specificity for negative symptoms (0.994 vs. 0.917).
  • Symptom prevalence varied across SARS-CoV-2 variant eras, with NLP identifying symptoms more accurately in COVID-19 positive patients.

Conclusions:

  • AI-based NLP is a highly effective tool for real-time COVID-19 symptom detection in pediatric EDs, surpassing traditional ICD-10 coding.
  • The study highlights the dynamic nature of COVID-19 symptom presentation across different virus variants.
  • Dynamic, technology-driven surveillance approaches are essential for managing emerging infectious diseases effectively.