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Artificial intelligence in differentiating tropical infections: A step ahead.

Shreelaxmi Shenoy1, Asha K Rajan1, Muhammed Rashid1

  • 1Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India.

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Summary

Developing a clinician-assisted tool using machine learning can improve the differentiation of tropical infections like dengue and malaria. This aids in earlier detection and better patient management.

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Area of Science:

  • Tropical medicine
  • Computational biology
  • Clinical decision support

Background:

  • Differentiating tropical infections is challenging due to similar clinical and laboratory presentations.
  • Sophisticated diagnostic tests and predictive tools are needed for accurate diagnosis.
  • This study addresses the need for a decision-making tool to distinguish common tropical infections.

Purpose of the Study:

  • To develop a clinician-assisted decision-making tool for differentiating common tropical infections.
  • To identify key clinical and laboratory parameters for differential diagnosis.
  • To evaluate the performance of statistical and machine learning models in infection differentiation.

Main Methods:

  • Cross-sectional study with a 9-item questionnaire for need analysis.
  • Retrospective study to identify significant differential parameters.
  • Development of a decision tree, multinomial logistic regression, and machine learning models.

Main Results:

  • Key predictors included sodium, bilirubin, albumin, lymphocytes, platelets, abdominal pain, arthralgia, myalgia, and urine output.
  • Multinomial logistic regression showed varying predictability (38%-66%) for different infections.
  • Binary classification machine learning models achieved higher predictability (79%-88%) compared to multi-classification models (55%-60%).

Conclusions:

  • This study is the first to simultaneously explore statistical and machine learning for tropical infection differentiation.
  • Machine learning techniques offer potential for early detection and improved patient care in tropical medicine.
  • The developed models show promise for enhancing diagnostic accuracy in resource-limited settings.