ckd

Rayman Control Panel

Containerized workflow for analysis of human diabetic kidney disease by snRNA-seq and snATAC-seq

🏥 AI-powered system for Chronic Kidney Disease staging and treatment. Combines a stacking ensemble (CatBoost, XGBoost, LightGBM) with clinical rules to achieve 96.8% accuracy. Includes SHAP explainability, a Streamlit web app, PDF reports, and expert-aligned treatment guidance. Built for clinicians, researchers, and ML developers in healthcare AI.

A Python library for kidney failure risk estimation using Tangri's KFRE model

Medical Utils

AI-driven multi-disease risk assessment platform using Machine Learning, Explainable AI, and secure medical data analysis. Supports Diabetes, CKD, Parkinson’s and clinical insights for early detection and better healthcare decisions.

Towards a CKD (Chronic Kidney Disease) dataset.

Real-world analysis of chronic kidney disease (CKD) in primary care in the Dominican Republic, integrating clinical and sociodemographic data with a reproducible Python pipeline.

营养学 Skill | 基于《成人慢性肾脏病食养指南（2024年版）》的AI科普对话助手。覆盖CKD 1-5期及透析期的食养原则、中医辨证食养、7地区84套食谱、食物营养成分表及营养管理方案。 · 使用WorkBuddy构建

GFR regression with honest validation: CKD_Status excluded to avoid leakage

Containerized workflow for single cell detection of loss of Y chromosome and other mosaic chromosomal alterations in chronic kidney disease

Open web services for dApps ▶ The @stackd backend

A hybrid clinical decision support system that combines machine learning (XGBoost, LightGBM) with eGFR-based rule logic to predict chronic kidney disease risk and determine clinical stage. Built with a leakage-free training pipeline, stratified cross-validation, and a Streamlit interface for single and bulk patient prediction.

Ckd with React is easy

A tool for deserializing different file formats from Just Dance 2015 (UbiArt Framework)

Predicting the hyper-local prevalence of chronic kidney disease with stochastic gradient boosting

A containerized workflow for analysis in aging rat single-cell study

Evaluating racial and/or ethnic disparities in the timeliness of nephrology care amongst patients with newly diagnosed CKD. This work will look at time to nephrology follow up once patients are diagnosed with CKD Stage 3 and CKD Stage 4.

Reproducible R analysis code for a cross-sectional OCT study of chronic kidney disease, hypertension, diabetes, and inner-retinal thickness at Koç University Hospital.