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

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

A Python package for standardizing medical 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 .

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

ScrapeMed: Data scraping for PubMed Central.

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

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

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

Extractive summarizationof medical transcriptions

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

https://arxiv.org/abs/2201.06499

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

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

MediChain-DApp is a decentralized application for securely managing medical records using blockchain technology. It allows patients to control access to their health data, while doctors can securely view and update medical records. Built on Ethereum and IPFS, MediChain ensures transparency, privacy, and data integrity.

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

This project builds a machine learning model to predict the survival of patients with hepatitis based on clinical and demographic features. It includes data preprocessing, training classification algorithms, evaluating model performance, and saving the final model for deployment.