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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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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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Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning.

Yuelyu Ji1, Yuhe Gao2, Runxue Bao3

  • 1Department of Information Science, School of Computing and Information, University of Pittsburgh, Pittsburgh,USA.

Proceedings. IEEE International Conference on Healthcare Informatics
|March 15, 2024
PubMed
Summary
This summary is machine-generated.

Identifying COVID-19 patients likely to revisit the emergency room (ER) is crucial. A new deep transfer learning model, Multi-DANN, effectively predicts 7-day ER revisits using Electronic Health Records (EHRs).

Keywords:
COVID-19deep transfer learningdomain adversarial neural networkemergency room revisitmultiple sources

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Public Health

Background:

  • Coronavirus disease 2019 (COVID-19) presents a significant global health challenge with varied clinical outcomes.
  • A substantial number of COVID-19 patients require emergency room (ER) readmission shortly after discharge, increasing healthcare burdens.
  • Early identification of patients at high risk for ER revisits is essential for optimizing medical resource allocation.

Purpose of the Study:

  • To develop and evaluate predictive models for identifying COVID-19 patients at high risk of ER revisits within 7 days of discharge.
  • To address data heterogeneity across multiple ERs using deep transfer learning techniques.
  • To assess the effectiveness of the Domain Adversarial Neural Network (DANN) algorithms in predicting patient revisits.

Main Methods:

  • Utilized Electronic Health Records (EHRs) from 3,210 COVID-19 patient encounters across 13 ERs.
  • Employed Natural Language Processing (NLP) with ScispaCy to extract key clinical concepts.
  • Developed 7-day revisit prediction models using frequent clinical concepts and compared Multi-DANN, Single-DANN, and baseline strategies.

Main Results:

  • The Multi-DANN model demonstrated superior performance in predicting 7-day ER revisits for COVID-19 patients, achieving a median AUROC of 0.8.
  • Multi-DANN significantly outperformed Single-DANN (median AUROC = 0.5) and baseline models, effectively handling domain differences.
  • The study confirmed the utility of EHR data in developing robust predictive models for ER revisit risk.

Conclusions:

  • Deep transfer learning, particularly the Multi-DANN approach, is highly effective in predicting COVID-19 patient ER revisits.
  • The Multi-DANN strategy successfully mitigates data heterogeneity issues from multiple sources, enhancing model generalizability.
  • This predictive capability can aid clinicians in proactive patient management and resource optimization within emergency departments.