Drug Conflict Detection

Overview

Prescribing multiple medications simultaneously carries significant risks due to adverse drug-drug interactions (DDIs). Standard conflict detection systems often flag hundreds of minor warnings, leading to “alert fatigue” where doctors might accidentally ignore critical, life-threatening conflicts.

Intelligent Detection

This project approaches the problem using Multi-Agent AI and Severity-Prioritized Search Algorithms:

• Context-Aware Agents: Instead of simple database lookups, the agents analyze patient profiles alongside the prescription list to contextually evaluate the risk of drug interactions.
• Severity Prioritization: The search algorithm is tuned to surface high-severity, contraindicating interactions first, suppressing low-priority noise so that healthcare providers can focus on what actually matters.
• Prototype UI: Deployed via Streamlit, allowing users to input a mock list of prescriptions and receive an instant, tiered breakdown of potential conflicts.

Technical Stack

• Python: Core logic and data processing.
• AI/LLMs: Intelligent agents utilized for nuanced understanding of drug interaction literature.
• Streamlit: Fast, responsive web interface for prototyping.