AI-Assisted Clinical Report Structuring System

Overview

This project is a Go-based backend application designed to manage medical patient records and unstructured clinical reports.
It extracts structured insights from clinical text using AI-assisted logic and stores the results in a relational SQL database.

The system focuses on data integrity, reliability, and safe medical data handling.

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Features

• Add and manage patient records
• Store unstructured clinical reports
• Extract structured information (diagnosis, key findings, severity)
• Enforce relational integrity using SQL foreign keys
• Console-based interface for simplicity and reliability

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Tech Stack

• Language: Go
• Database: MySQL
• Concepts: SQL, Foreign Keys, Backend Development, AI-assisted NLP
• Tools: Git, GitHub

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Database Design

• patients – stores patient details
• reports – stores raw clinical report text
• extracted_data – stores structured insights derived from reports

All reports are linked to patients using foreign key constraints.

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How AI Is Used

AI-assisted text processing is used to convert unstructured clinical reports into structured data fields. The system is designed to avoid medical diagnosis or predictions and focuses purely on data structuring.

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How to Run

1. Set up MySQL and create the database
2. Update database credentials in the Go code
3. Run the application:

go run .