End Stage Renal Disease

Predictors of Anemia in Ethiopia: A Systematic-Review of Machine Learning Approaches

Anemia remains a critical public health issue globally which is disproportionately affecting population in low- and middle income countries, with sub-Saharan Africa particularly Ethiopia experiencing high prevalence rates, Despite ongoing interventions, understanding the multifactorial causes of anemia and enhancing predictive Modelling through modern analytic approaches remains limited. This syst...

Application of Machine Learning (ML) to Predict Under-Five Anemia using the 2018 Zambia Demographic and Health Survey (ZDHS)

Accurate prediction of the risk of anemia in under five children using ML can help reduce the burden of anemia in Zambia. This study applied ML models to predict the risk of anemia in under five children in Zambia. This cross sectional study utilized data from the 2018 ZDHS. Feature selection was performed using the Boruta algorithm. Several ML models were trained on 80% of the data set. The best ...

Incidence, Outcomes and Risk Factors of Cardiac Arrest Among Surgical Patients in the UK Biobank: A Population-Based Cohort Study

Perioperative cardiac arrest (CA) is a devastating surgical complication, yet its epidemiology and risk factors across diverse surgical populations ar...

Symbolic Regression for Mycophenolic Acid Dosage Prediction in Kidney Transplant Recipients

Chronic kidney disease (CKD) affects millions worldwide and often progresses to end-stage renal disease (ESRD), for which kidney transplantation remai...

Development of Machine Learning Models to Predict Hypoglycemia and Hyperglycemia on Days of Hemodialysis in Patients with Diabetes based on Continuous Glucose Monitoring

Patients with diabetes undergoing hemodialysis (HD) are at risk of asymptomatic hypo- and hypergly-cemia within 24 hours of dialysis. Continuous gluco...

Does LLM Assistance Improve Healthcare Delivery? An Evaluation Using On-site Physicians and Laboratory Tests∗

We deployed large language model (LLM) decision support for health workers at two outpatient clinics in Nigeria. For each patient, health workers draf...

Precision Immunosuppression and Long-Term Kidney Transplant Outcomes: A Dual Survival Modeling Framework

Optimizing immunosuppressive therapy remains central to improving long-term outcomes after kidney transplantation. Both induction and maintenance ther...

Nationwide Spatiotemporal Dynamics and Machine Learning Prediction of Anemia Among Women in Lesotho, 2023–2024

Despite substantial efforts, anemia continues to pose a significant public health challenge, disproportionately affecting women of reproductive age. I...

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

OBJECTIVES: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for ...

Formalization of Biological Circuit Block Diagrams for formally analyzing Biomedical Control Systems in pHRI Applications

The control of Biomedical Systems in Physical Human-Robot Interaction (pHRI) plays a pivotal role in achieving the desired behavior by ensuring the ...

Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

Sickle cell anemia, which is characterized by abnormal erythrocyte morphology, can be detected using microscopic images. Computational techniques in...

Predicting Survival of Hemodialysis Patients using Federated Learning

Hemodialysis patients who are on donor lists for kidney transplant may get misidentified, delaying their wait time. Thus, predicting their survival ...

Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning

Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that lear...

Influence of vitamin D and calcium-sensing receptor gene variants on calcium metabolism in end-stage renal disease: insights from machine learning analysis.

OBJECTIVE: End-stage renal disease (ESRD) commonly manifests with disrupted calcium balance, leading to renal osteodystrophy. We posited that variatio...

Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach

Objective: To improve prediction of Chronic Kidney Disease (CKD) progression to End Stage Renal Disease (ESRD) using machine learning (ML) and deep ...

Early identification of patients at risk for iron-deficiency anemia using deep learning techniques.

OBJECTIVES: Iron-deficiency anemia (IDA) is a common health problem worldwide, and up to 10% of adult patients with incidental IDA may have gastrointe...

Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection.

OBJECTIVE: To develop and evaluate a novel feature selection technique, using photoplethysmography (PPG) sensors, for enhancing the performance of dee...

A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.

Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The...

Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques.

This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to pr...

Understanding the Manufacturing Process of Lipid Nanoparticles for mRNA Delivery Using Machine Learning.

Lipid nanoparticles (LNPs), used for mRNA vaccines against severe acute respiratory syndrome coronavirus 2, protect mRNA and deliver it into cells, ma...