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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Data Mining Models for Automatic Problem Identification in Intensive Medicine.

Inês Quesado1, Julio Duarte1, Álvaro Silva2

  • 1Algoritmi/LASI research center, University of Minho, Portugal.

Procedia Computer Science
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

Data mining models were developed to predict COVID-19 diagnosis by analyzing patient vital signs. These models show promising results in supporting clinical decisions for accurate COVID-19 detection.

Keywords:
ClassificationData MiningIntensive Care UnitIntensive MedicineVital Signs

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Area of Science:

  • Medical informatics
  • Data mining
  • Computational biology

Background:

  • Accurate and timely diagnosis of COVID-19 is crucial for effective patient management and public health.
  • Predictive models can aid healthcare professionals in decision-making processes for disease diagnosis.

Purpose of the Study:

  • To develop and evaluate Data Mining (DM) models for predicting COVID-19 diagnosis.
  • To assess the correlation between patient vital signs and COVID-19 diagnosis.

Main Methods:

  • Data collected from bedside monitors and electronic nursing records in an Intensive Care Unit.
  • DM models induced using algorithms: Decision Trees, Random Forest, Naive Bayes, and Support Vector Machine.
  • Model performance evaluated using sensitivity, specificity, and accuracy metrics.

Main Results:

  • The developed DM models demonstrated promising accuracy in predicting COVID-19 diagnosis.
  • Analysis identified key vital signs correlated with COVID-19 diagnosis.
  • The study highlights the potential of DM techniques in supporting clinical decision-making.

Conclusions:

  • Data mining models can effectively support medical decision-making for COVID-19 diagnosis.
  • Vital signs analysis using DM techniques offers a valuable tool for early disease detection.
  • Further research can refine these models for broader clinical application.