An open workbench for leakage-aware, calibrated, explainable risk modeling on longitudinal EHR-style data — built for students, labs, and health-informatics research.

Point of care system for AMPATH clinics

Social platform for people with chronic diseases

This project aims to develop an embedded system that monitors chronic disease patients who need frequent medical check-ups.

Deep learning for comprehensively assessing chronic gastritis from whole-slide images: a multicenter, retrospective cohort study

This site provides information to support data users with developing custom queries in the Multi-State EHR-Based Network for Disease Surveillance (MENDS).

Human survival post-LGM relied on adaptation, cooperation, and innovation. Neanderthal and Denisovan genes shaped immunity and metabolism, but uniform modern diets clash with these traits, fueling chronic disease. Embracing diversity can drive health and resilience.

A Do-It-Yourself AI-based module for your personalized interstitial glucose 30-min prediction! Compatible with different OS through Dockerization. Your data, your personalized model, and the execution are performed locally, without sharing anything with anyone! This module has been validated with real CGM data collected from 29 people with T1D.

BioMedical Language Processing with ELECTRA

Chronic Disease Control Data Analysis and Visualization

My first repository on GitHub

Information on Thriving with Long-COVID and Other Chronic Illness

Analysis of publicly available data to identify areas with food access challenges. Study of a link between food insecurity and prevalence of chronic conditions.

Predicting Chronic Disease Onset in Older Adults: A machine learning study using The Irish Longitudinal Study on Ageing (TILDA)

Tealth provides actionable, accessible health insights for predicted long-term health risks, powered by ML.

Final Project for Geospatial Datascience in Python

Open-access scholarly review on multimorbidity / Открытый научный обзор по мультиморбидности

This repository contains an EDA dashboard and some key visualizations providing insights into the US Chronic Disease Indicator dataset and disease trends

Chronic disease prediction and progression analysis using statistical modeling and machine learning in R.

it's a JAVAFX desktop application about chronic diseases , where we manage three user roles : admin , doctor and patient . (ESPRIT School project)