Medical Diagnosis Engine

Medical Diagnosis Engine

Inspiration I was inspired by the fact that doctors, despite having medical records and testing systems, often face difficulty in making fast and accurate decisions for treating heart diseases. After research, I found that the problem is not a lack of medical expertise, but the gap between medical data and making optimized decisions in a timely manner.
I transformed the mindset of a cardiologist into a system that converts medical data and tests into comprehensive diagnosis and analysis with supportive recommendations – this is the Medical Diagnosis Engine.

What It Does

• Core Engine: Converts medical data (symptoms, medical history, vital signs, test results) into direct heart indicators such as blood pressure, heart rate, cholesterol levels, and triglycerides.
• Diagnostic Engine: Identifies the root cause of each case (hypertension, cholesterol imbalance, arterial diseases, heart rhythm problems).
• Decision Support Engine: Generates low-risk supportive recommendations for each case, such as adjusting medications, nutrition plans, or specific exercises.
• Artificial Intelligence: Analyzes additional measurable indicators (blood pressure trends, cholesterol fluctuations, repeated lab test results, patient treatment response).
• Smart MNEE Layer (Medical Numeric Event Engine): Can convert some decisions directly into programmable actions such as:
  • Automatically scheduling appointments once tests are completed.
  • Automatically ordering medications from the pharmacy upon prescription.
  • Sending patient follow-up notifications upon critical changes in indicators.

How It Was Built

• Frontend: React + Next.js + Tailwind CSS for interactive and flexible dashboards.
• Backend: Python + FastAPI for processing medical data, calculating indicators, and executing analysis and diagnostic engines.
• Database: PostgreSQL to store patients' historical data, diagnoses, optimization decisions, and performance indicators.
• Artificial Intelligence: Supports data analysis and provides recommendations to improve treatment while preserving the core engine's role in diagnosis and analysis.
• MNEE Integration: Allows the system to execute automated digital actions such as scheduling appointments or treatment follow-up in a secure and transparent way.

User Interface and Doctor's Role

• An interactive dashboard displays all system layers: data, vital indicators, automated diagnosis, and supportive recommendations.
• The doctor is the final decision-maker: they can review, modify, or reject any recommendation before execution.
• Full oversight of all layers ensures immediate review of any errors.
• Smart automation under doctor supervision: AI and MNEE suggest improvements such as scheduling appointments or ordering medications, but execution only happens with approval.
• Result: The doctor controls everything, while AI and digital support enhance decisions safely and efficiently.

Application Workflow Data Entry ⟶ Data Validation ⟶ Heart Indicator Calculation ⟶ Medical Diagnosis ⟶ Decision Support Indicators ⟶ Risk Classification ⟶ Generating Optimized Recommendations ⟶ Risk Management ⟶ Governance ⟶ Execution and Monitoring ⟶ Strategic Impact ⟶ Automated MNEE Digital Actions

Challenges

• Simulating a cardiologist’s mindset within a multi-layer engine.
• Training AI to provide accurate recommendations without affecting the core diagnosis.
• Integrating MNEE to manage appointments, medications, and medical measurements safely, transparently, and automatically.

Achievements

• A comprehensive medical engine that identifies root problems and generates actionable recommendations.
• Integration of AI and MNEE layers to support smart improvements and patient follow-up.
• Interactive user interface displaying all layers for decision review and practical learning.

Lessons Learned

• Combining a multi-layered engine with AI and automated digital actions.
• Maintaining diagnostic accuracy while automating treatment and appointment follow-up.
• The ability to scale the system to large hospitals and multiple clinics, linking to digital medical data.

Next Steps

• Deeper integration with Hospital Information Systems (HIS) and APIs for labs and pharmacies.
• Developing more performance indicators to enhance optimized recommendations.
• Expanding the system to cover all heart diseases and chronic conditions, with fully automated patient follow-up.
• Adding an Institutional Adaptation Layer: to adapt the system for hospitals, clinics, and health centers, standardize raw data into a unified model, and enhance diagnostic accuracy and decision-making. """

Built With

• ai
• automation:
• css
• fastapi
• frontend:
• next.js
• postgresql
• python