ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)
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Updated
Oct 17, 2022 - Jupyter Notebook
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)
Conversational Question Answering on Clinical Text
Repository for the journal article, 'FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices', Mahbub Ul Alam, Rahim Rahmani. Sensors 23, no. 2: 970, https://doi.org/10.3390/s23020970.
This repository contains a 4-bit quantized ClinicalBERT model for disease classification based on clinical text. Inspired by CheXNet, this model can predict diseases from patient symptom descriptions, particularly focusing on chest-related conditions.
Early Prediction of Sepsis using Time Seiries Forecasting (Published at 2023 IEEE AI4Health)
AI-powered prediction of in-hospital mortality and 30-day readmission using MIMIC-III clinical data. Combines structured features and ClinicalBERT embeddings with XGBoost/CatBoost Best Performance models for accurate, explainable healthcare forecasting.
PatientINF embedding models - with forum extraction and model building scripts
Système multimodal d'aide à la décision médicale développé sous TensorFlow/Keras, traitant des données image, tabulaire, texte et audio.
Fine-tuned ClinicalBERT model for predicting diseases from natural language symptom descriptions.
Explainable polypharmacy ADR prediction using heterogeneous biomedical knowledge graphs (PrimeKG + Decagon) and ClinicalBERT-attributed graph neural networks. MTech thesis, IIT Patna 2026.
Progressive ML pipeline for medical claim denial prediction from structured baselines (LR/XGBoost+SHAP) through ClinicalBERT clinical-note embeddings to a FAISS retrieval-augmented agentic layer. Production-style monorepo with MLflow tracking, Docker Compose, FastAPI serving, and CI.
An advanced AI Medical Chatbot featuring a Hybrid RAG architecture, ClinicalBERT embeddings, smart allergy filtering, and emergency failover for precise healthcare assistance.
AI in Healthcare, Stanford Medicine
Clinical NLP and machine learning project for classifying sickle cell emergency department visits into admission and high-risk categories.
A Medical Chatbot using ClinicalBERT and seq2seq
GenAI’s 2nd Opinion
A multimodal machine learning project to predict 30-day hospital readmission using a RAG-LLM pipeline (ClinicalBERT, FAISS, Mistral-7B) and structured data (ML models).
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