resources about federated learning and privacy in machine learning

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

Research data management in biomedical and machine learning applications

HL7 FHIR client library. Sync and async crud operations against R4 FHIR servers. Resource validation & serialization

A Python package for standardizing medical data

watchlib is a Python module providing tools for loading, analyzing and visualizing Apple Watch health data.

This repository includes API to get ICD-10 codes from descriptions. The Model is a transformer based on BERT. The embeddings are being finetuned based on the following paper: https://arxiv.org/abs/1904.03323 .

ScrapeMed: Data scraping for PubMed Central.

I list all the medical data sets I find in my researches.

Concept Library - a system for storing, managing, sharing and documenting clinical code lists in health research through Phenotypes.

A Python bootcamp for medical students designed to teach basic DICOM and imaging data handling skills.

Extractive summarizationof medical transcriptions

Developed an NLP classifier for detecting medical domains in texts using a Siamese Neural Network

Unified Python SDK for OpenFDA, PubMed, and ClinicalTrials.gov with clinical intelligence, interaction detection, and research tools.

A private blockchain developed in Ethereum for a network of Raspberry Pis

https://arxiv.org/abs/2201.06499

This project implements a machine learning-based triage system for emergency rooms, which classifies patients based on their symptoms and vitals using a Random Forest Classifier. The system features real-time patient data integration, a user-friendly GUI built with Tkinter, and secure patient data encryption using Fernet from the cryptography lib

LightGBM no-show predictor + AWS deployment (EC2 + Elastic Beanstalk)

Get started building safe, reliable, and production-grade multi-agent systems for medical applications!

Risk Factor Analysis for Medical Data, Open-source Machine Learning Platform