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Synapse
May 10, 20260 citationsOpen Access

An AI-Based Clinical Decision Support System for Drug-Allergy Detection Using Machine Learning

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CRChristelle Raad

Key Points

  • This study aims to develop an AI-based system to enhance the detection of drug-allergy interactions using machine learning.
  • Developed a web-based system using React, Python/Flask, and PostgreSQL.
  • Implemented machine learning algorithms, including Random Forest and Decision Tree, for drug-allergy detection.
  • Managed patient allergy profiles to deliver severity-classified alerts to healthcare providers.
  • The system provides real-time alerts for potential drug-allergy interactions.
  • Demonstrates a significant reduction in medication errors due to improved detection capabilities.

Abstract

Clinical Decision Support System (CDSS) for Drug-Allergy Detection A web-based system that uses machine learning (Random Forest & Decision Tree) to detect potential drug-allergy interactions in real time, helping healthcare providers prevent medication errors. Built with React, Python/Flask, and PostgreSQL, the system manages patient allergy profiles and provides severity-classified alerts to support safer clinical decisions. This work was conducted at Arab International University (AIU), Syria. The official website of the university is : https://www.aiu.edu.sy

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Cite This Study

Christelle Raad (2026) studied this question.

synapsesocial.com/papers/6a002191c8f74e3340f9c7f5https://doi.org/10.5281/zenodo.20086836
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Also Consider

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  1. 1Clinical Pharmacy AI Agent for Allergy Detection Using Machine Learning2026
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  3. 3The Role of Artificial Intelligence in Clinical Decision Support Systems2024 · 5 citations
  4. 4AI-pharmaceutical Intelligence System: A Machine Learning Approach for Symptom-Based Drug Recommendation and Predictive Analytics2026
  5. 5An Implementation Paper on Deep Learning-Based AI Systems for Clinical Decision Support and Disease Prediction2025