Medi-Match: AI-Driven Hospital Resource Optimization & Triage

Medi-Match is an advanced healthcare management platform that integrates a modern WPF (.NET) frontend with a powerful Python-based AI intelligence layer.
The system addresses the complex problem of hospital resource allocation and emergency patient prioritization using Evolutionary Computing, Fuzzy Logic, and Rule-Based Expert Systems.

Group Members

• Afia Aziz — ID: 231561
• Zumer Dhillun — ID: 231597
• Zoya Azad — ID: 231579

Project Summary

In modern healthcare environments, manual scheduling of patients to doctors is inefficient and prone to human error.
Medi-Match automates the patient-to-doctor assignment process by intelligently analyzing urgency, specialty requirements, and doctor workload.

Core Features

• AI Scheduler
  Matches patients to doctors based on specialty, workload, and urgency.

• Emergency Triage System
  Evaluates patient symptoms to calculate an urgency score (1–10) and recommends an appropriate specialist
  (e.g., Cardiologist for chest pain).

• Optimization Engines

  • Fast Heuristic Scheduler
  • Globally optimal Genetic Algorithm Scheduler

• Data Visualization
  Real-time convergence graphs showing AI performance.

• Professional Reporting
  Automated HTML and Text-based clinical triage reports.

AI Techniques Used & Justification

1. Fuzzy Logic (Urgency Normalization)

Implementation:
The calculate_fuzzy_score function converts discrete urgency levels (1–10) into a continuous fuzzy range ([0, 1]).

Justification:
Medical urgency is not binary. Fuzzy logic handles uncertainty and allows smoother prioritization of patients.

2. Genetic Algorithm (Evolutionary Optimization)

Implementation:
Implemented in scheduler_ga.py using:

• Tournament Selection
• Two-Point Crossover
• Mutation
• 120 Generations

Justification:
Hospital scheduling is an NP-Hard problem. Genetic Algorithms efficiently explore large search spaces to find globally optimal schedules.

3. Rule-Based Expert System (Clinical Mapping)

Implementation:
Uses a clinical knowledge base (SPECIALTY_CONDITIONS) to map symptoms and diseases to medical specialties.

Justification:
In healthcare, transparency is critical. Rule-based systems provide explainable and auditable decision-making.

4. Heuristic Greedy Search

Implementation:
A scoring-based heuristic algorithm that selects locally optimal assignments.

Justification:
Provides fast, real-time scheduling when deep optimization is not required.

Knowledge Representation

1. Semantic Network Diagram

Represents relationships between:

• Symptoms
• Medical Departments
• AI Scheduling Logic

2. Frame-Based Representation

Defines medical entities using slots and fillers:

• Patient
• Doctor
• Triage

Setup & Run Instructions

Prerequisites

• Visual Studio 2022 (with .NET Framework 4.7.2)
• Python 3.8+ (added to System PATH)

Required Python Libraries

pip install numpy matplotlib

Running the Project

1. Clone the Repository

git clone https://github.com/YourUsername/Medi-Match.git

2. Open the Solution

Open the .sln file in Visual Studio.

3. Build

Clean and rebuild the solution.

4. Run

Press F5 to launch the WPF application.

Application Usage

• Triage Module: Assess emergency patients and generate clinical reports.

• Scheduler Module: Enter hospital resources and run AI-based optimization.

• Graphs Page: View convergence graphs showing AI performance.

Project Structure

HospitalSchedulerUI/
│
├── WPF Frontend (C# / XAML)
│
├── Backend/
│   └── PythonScripts/
│       ├── scheduler.py          # Heuristic Scheduler
│       ├── scheduler_ga.py       # Genetic Algorithm
│       └── triage_calculator.py  # Emergency Triage Logic
│
├── Results/
│   ├── output.json
│   ├── metrics.csv
│   └── convergence.png

Sample Result Contract

The system ensures reliability using a strict JSON input/output contract.

Sample input.json

{
  "Doctors": 3,
  "Patients": 5,
  "DoctorDetails": [
    { "Name": "Dr. Smith", "Specialty": "Cardiology" }
  ],
  "Urgency": [9, 4, 2, 8, 5]
}

License

This project was developed as part of the Artificial Intelligence course and is distributed for educational purposes only.