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An ensemble-based sentiment analysis approach for precision medicine recommendation.

Anjana Mishra1, Sukant Kishoro Bisoy1, D Samuel Kollie2

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Summary

This study introduces a new AI framework for personalized medicine recommendations using patient reviews and clinical data. The SCL-MedStacker achieved 92% accuracy, improving clinical decision-making and treatment effectiveness.

Keywords:
Deep learning healthcareEnsemble learningMedicine recommendationNatural language processingPersonalized medicineSentiment analysis

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Informatics
  • Computational Medicine

Background:

  • The proliferation of online health platforms generates vast amounts of unstructured patient review data.
  • Extracting clinically relevant information from these diverse datasets is a significant challenge for personalized medicine.
  • Existing methods struggle to integrate patient-generated data with demographic and clinical attributes.

Purpose of the Study:

  • To develop a context-aware framework for personalized medicine recommendations by integrating patient reviews with demographic and clinical data.
  • To enhance the precision and reliability of medicine recommendations.
  • To improve clinical decision-making and patient outcomes.

Main Methods:

  • A novel stacked ensemble framework (SCL-MedStacker) combining deep learning (DL) models (SNN, CNN, RNN_LSTM) and a Random Forest classifier.
  • Integration of patient reviews with demographic (age, gender) and clinical attributes (medical condition).
  • Application of random oversampling to address class imbalance in the medical review dataset.

Main Results:

  • The SCL-MedStacker framework achieved a high accuracy of 92%, surpassing existing state-of-the-art methods.
  • The personalized recommendation mechanism demonstrated enhanced contextual relevance and predictive reliability.
  • The study successfully addressed the challenge of extracting meaningful insights from unstructured patient data.

Conclusions:

  • The proposed framework offers a promising approach for personalized medicine recommendations, leveraging patient reviews and clinical data.
  • This AI-driven system has the potential to support clinical decision-making, reduce prescribing errors, and improve treatment effectiveness.
  • The SCL-MedStacker framework represents a significant advancement in utilizing big data for precision medicine.