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.
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.
Early Prediction of Sepsis using Time Seiries Forecasting (Published at 2023 IEEE AI4Health)
Système multimodal d'aide à la décision médicale développé sous TensorFlow/Keras, traitant des données image, tabulaire, texte et audio.
Explainable polypharmacy ADR prediction using heterogeneous biomedical knowledge graphs (PrimeKG + Decagon) and ClinicalBERT-attributed graph neural networks. MTech thesis, IIT Patna 2026.
PatientINF embedding models - with forum extraction and model building scripts
Fine-tuned ClinicalBERT model for predicting diseases from natural language symptom descriptions.
Clinical NLP and machine learning project for classifying sickle cell emergency department visits into admission and high-risk categories.
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.
AI in Healthcare, Stanford Medicine
An advanced AI Medical Chatbot featuring a Hybrid RAG architecture, ClinicalBERT embeddings, smart allergy filtering, and emergency failover for precise healthcare assistance.
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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