🧠 NLP-Based Automated Cleansing for Healthcare Data

A powerful Natural Language Processing (NLP) project focused on automating data cleansing in healthcare systems. This tool leverages NLP techniques to detect, extract, and clean unstructured or inconsistent data, ensuring higher accuracy and standardization across healthcare records.

🏥 Project Overview

Healthcare data often contains noise, redundancy, typos, and inconsistencies, making it challenging to use for analysis, patient monitoring, and research. Our solution automatically identifies anomalies, normalizes terminology, and standardizes data using machine learning and linguistic patterns.

🔍 Features

• 🩺 Intelligent cleansing of patient records, clinical notes, and medical data
• 🧬 Named Entity Recognition (NER) for symptoms, diagnoses, and medications
• 🧹 Automatic removal of duplicates, typos, and irrelevant text
• 📊 Pre-processing pipeline for structured EHR integration
• ⚙️ Custom dictionaries and medical vocabulary support
• 🧠 Trained on healthcare-specific corpora for domain accuracy

🛠 Tech Stack

• Language: Python
• Libraries: spaCy, NLTK, scikit-learn, pandas
• Tools: Jupyter Notebook, Flask (optional UI/API layer)