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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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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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A GPT-based EHR modeling system for unsupervised novel disease detection.

Boran Hao1, Yang Hu1, William G Adams2

  • 1Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.

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|August 9, 2024
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Summary

An AI model using Generative Pre-trained Transformer (GPT) can detect novel diseases and predict outbreaks by analyzing patient Electronic Health Records (EHR). This AI complements physicians in early detection and personalized treatment planning.

Keywords:
Deep learningEHR modelingGPTNovel disease detectionPandemic prevention

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Epidemiology

Background:

  • Early detection of novel diseases and emerging outbreaks is crucial for public health.
  • Physician-led anomaly detection can be enhanced by advanced computational tools.
  • Electronic Health Records (EHR) contain vast data for disease pattern recognition.

Purpose of the Study:

  • To develop an Artificial Intelligence (AI)-based anomaly detection model.
  • To complement physician expertise in identifying novel disease cases within hospitals.
  • To prevent emerging infectious disease outbreaks through early detection.

Main Methods:

  • A Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was developed.
  • The system modeled hospitalized patients' Electronic Health Records (EHR) using Empirical Risk Minimization (ERM).
  • Methods inspired by Large Language Models (LLMs) were used to compute Out-Of-Distribution (OOD) anomaly scores.

Main Results:

  • The GPT model predicted Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) hospitalizations with 92.2% Area Under the ROC Curve (AUC) in an unsupervised setting.
  • Individual patient anomaly detection and mortality prediction achieved AUCs of 78.3% and 94.7%, outperforming linear models.
  • The model captured diverse SARS-CoV-2 clinical trajectories and offered interpretable detections.

Conclusions:

  • A GPT model can accurately detect emerging outbreaks within hospitals by analyzing EHR time sequences.
  • The AI system can identify anomalous patient cases where outcomes deviate from model predictions.
  • The GPT model's ability to forecast clinical variables aids in personalized treatment planning.