End Stage Renal Disease

Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach.

BACKGROUND: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for analyzing dietary associations and uncovering intricate patterns within data. We explored dietary patterns associated with serum iron biomarkers and vitamin D among pregnant women, utilizing MLA to perform predictive analyses.

Characterization Of Diseases In Temporal Comorbidity Networks

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used pub...

Development of Machine-Learning-Based Models for Detection of Cognitive Impairment in Patients Receiving Maintenance Hemodialysis.

BACKGROUND: Cognitive impairment is common but frequently undiagnosed in the dialysis population. We aimed to develop and validate a quick and accurat...

Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app.

The goal of this study was to expand our previously created prediction tool (PREDICT-AVF) and web app by estimating long-term primary and secondary pa...

Identification and validation of inflammatory response genes linking chronic kidney disease with coronary artery disease based on bioinformatics and machine learning.

Coronary artery disease (CAD) commonly occurs and elevates the risk of cardiovascular events and mortality in chronic kidney disease (CKD) patients. T...

Text mining for case report articles on "peritoneal dialysis" from PubMed database.

INTRODUCTION: The number of published medical articles on peritoneal dialysis (PD) has been increasing, and efficiently selecting information from num...

Leveraging artificial intelligence chatbots for anemia prevention: A comparative study of ChatGPT-3.5, copilot, and Gemini outputs against Google Search results.

AIM: This study evaluated the understandability, actionability, and readability of text on anemia generated by artificial intelligence (AI) chatbots.

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and bone marrow failure. ICU admission in these patie...

Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.

Formulas based on red blood cell indices have been used to differentiate between iron deficiency anemia (IDA) and thalassemia (Thal). However, they ex...

Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients.

Intradialytic hypotension (IDH) is a critical complication in patients with chronic kidney disease undergoing dialysis, affecting both patient safety ...

Preparing for Vascular Surgery Board Certification: A Comparative Study Using Large Language Models.

Introduction and aim Large language models (LLMs) are transforming medical education by offering innovative methods to enhance teaching and learning. ...

[Study on lightweight plasma recognition algorithm based on depth image perception].

In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Appl...

Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.

PURPOSE: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimatio...

ORAKLE: Optimal Risk prediction for mAke30 in patients with acute Kidney injury using deep Learning

Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). Th...

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a severe hematologic disorder marked by pancytopenia and bone marrow failure. ICU admission often reflects disease progression or c...

Predicting Levels of Anemia among Adolescents in Ethiopia Using homogeneous ensemble Machine Learning algorithm

Anemia significantly impacts adolescent girls’ health and quality of life in Ethiopia. Effective interventions require identifying key risk factors an...

RNAseq-Based Machine Learning Models for Prognostication of Multiple Myeloma

Multiple myeloma (MM) is characterized by abnormal plasma cell proliferation in the bone marrow, leading to symptoms like osteolytic lesions, anemia, ...

Octopi 2.0: Point-of-care Multi-disease Detection and Diagnosis via Edge AI Imaging Platform

Access to quantitative, robust, and affordable diagnostic tools is essential to address the global burden of infectious diseases. While manual microsc...

Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines

The analysis of complex biomedical datasets is becoming central to understanding disease mechanisms, aiding risk stratification and guiding patient ma...

Screening for anemia using multi-modal machine learning models on smartphones: protocol for a comparative accuracy study in rural India

Anemia, or low blood hemoglobin (Hb), affects one third of the world population, and is particularly prevalent in women and children in lower resource...