Add files via upload

.gitignore

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+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# production
+/build
+
+# misc
+.DS_Store
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*

.vscode/settings.json

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+{
+    "liveServer.settings.port": 5501
+}

README.md

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-## PROBLEM STATEMENT - 1
-# AI-Driven Boolean Query Generator for Healthcare
-
-Efficient information retrieval is essential in healthcare for advancing research, improving diagnostics, and supporting clinical decision-making. However, creating accurate Boolean queries to navigate complex medical databases can be challenging due to the vastness of data and specialized medical terminology. This project aims to develop an AI-driven solution that automates the creation of precise, context-aware Boolean queries tailored to specific healthcare needs.
-
-## Problem Statement
-
-In the healthcare industry, accessing and extracting relevant information from extensive medical databases is critical for:
-
-- Conducting medical research
-- Diagnosing conditions
-- Making informed treatment decisions
-
-However, the complexity of medical terminology and the volume of healthcare data make it difficult to manually formulate accurate Boolean queries. Inaccurate queries can lead to missed data points or overwhelming amounts of irrelevant information, impeding healthcare professionals’ ability to retrieve actionable insights. 
-
-## Solution Overview
-
-### Deliverables
-
-Develop an AI-based solution that automates Boolean query generation for healthcare applications. This system will leverage natural language processing (NLP) and AI techniques to create precise and contextually relevant queries that can:
-
-- Retrieve research studies or clinical trial data related to specific topics.
-- Filter medical records by patient symptoms or conditions.
-- Identify drug interactions or adverse events for specific treatments.
-- Access diagnostic data based on combinations of symptoms and lab results.
-
-### Objectives
-
-1. **Simplification of Medical Terminology**  
-   - Use NLP techniques to parse and simplify complex medical terms and map them to structured Boolean logic.
-
-2. **Context-Aware Query Generation**  
-   - Leverage AI algorithms to ensure queries are precise and contextually relevant, minimizing false positives and negatives.
-
-3. **Adaptability to Evolving Datasets**  
-   - Enable the system to adapt as healthcare data and medical knowledge expand, maintaining relevance over time.
-
-## Key Features
-
-- **NLP-Powered Terminology Parsing**: Automatically simplifies and maps complex medical terms for Boolean logic.
-- **AI-Driven Query Precision**: Uses context-aware algorithms to avoid irrelevant results and retrieve only pertinent data.
-- **Continuous Learning from Data**: Incorporates new medical knowledge and evolving datasets for sustained accuracy.
-- **Flexible Query Templates**: Supports different healthcare information needs, including research data, patient records, drug interactions, and diagnostic results.
-- **Symptom & Diagnostic Data**: Access data for complex cases involving multiple symptoms and lab results.
-- **Research Studies & Clinical Trials**: Retrieve studies relevant to specific treatments, conditions, or patient demographics.
-
----
-
-### Note: This serves only as a reference example. Innovative ideas and unique implementation techniques are highly encouraged and warmly welcomed!