A Label Attention Model for ICD Coding from Clinical Text

AI-powered system for mapping clinical text to Human Phenotype Ontology (HPO) terms using Retrieval-Augmented Generation (RAG). Features Python CLI/library, FastAPI backend, and Vue.js frontend for interactive phenotype extraction from medical texts.

Extract Gleason scores from texts.

AI-powered medical text correction and standardization service. Designed to transform raw clinical notes into clear, structured, and medically accurate documents. Built for doctors, clinics, telemedicine platforms, and digital health products — with easy integration via bots or APIs.

Production-ready Clinical NER Pipeline using BioBERT for medical entity extraction from clinical notes — 95%+ accuracy | QuantumHelix.ai

Framework for Bengali Clinical Text Classification

Machine learning and NLP system for automatic classification of Spanish/Catalan clinical diagnosis text into ICD-10 codes using TF-IDF features, LinearSVC, medical abbreviation expansion, and ICD dictionary augmentation.

This project explores a medical text dataset, evaluates named entity recognition approaches, and compares baseline and transformer-based models for the main downstream task.

🩺 MediScan-AI - Lightweight Local Medical Text Intelligence Analysis Engine | 轻量级本地医疗文本智能分析引擎 — Zero External Model Dependencies, NER, PII Masking, Record Parsing, Drug Interaction Check, Web UI

Topic modeling on clinical medical transcription text (mtsamples.com), comparing NMF and LDA to discover higher-level medical categories from unstructured notes.