End Stage Renal Disease

A machine learning tool for early identification of celiac disease autoimmunity.

Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical records of Maccabi Healthcare Services, we developed and evaluated five machine learning models to classify patients as at-risk for CD autoimmunity prior ...

Hope for the best prepare for the worst: acute kidney disease and catastrophic comorbidities (a case report).

It is evident that Acute Kidney Injury (AKI) is an independent risk factor for both the survival of patients and their kidneys. Here, we present a case of oliguric AKI secondary to blunt trauma-induced crush syndrome complicated with severe sepsis in which the patient had a complete recovery of kidney functions after 3 months of dialysis dependency. A 40-year-old male construction worker had a sev...

Very low doses of rituximab in autoimmune hemolytic anemia-an open-label, phase II pilot trial.

INTRODUCTION: Although rituximab is approved for several autoimmune diseases, no formal dose finding studies have been conducted. The amount of CD20+ ...

Development and Validation of a Multimodal Machine Learning Model for Diagnosing and Assessing Risk of Crohn's Disease in Patients With Perianal Fistula.

BACKGROUND: Diagnosis of Crohn's disease (CD) can pose challenges, particularly when perianal fistula is the initial presentation.

A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.

BACKGROUND: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by ...

Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.

BACKGROUND: Anemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers pr...

elimination of antimicrobials during ADVanced Organ Support hemodialysis.

BACKGROUND: Acute kidney injury (AKI) requiring continuous renal replacement therapy is common in critically ill patients. The ADVanced Organ Support ...

Corrigendum: Apolipoprotein A-I attenuates peritoneal fibrosis associated with peritoneal dialysis by inhibiting oxidative stress and inflammation.

[This corrects the article DOI: 10.3389/fphar.2023.1106339.].

Genotype-negative multiple endocrine neoplasia type 1 with prolactinoma, hyperparathyroidism, and subclinical Cushing's syndrome accompanied by hyperglycemia: a case report.

BACKGROUND: Multiple endocrine neoplasia type 1 (MEN1) is a rare autosomal dominant disorder, accompanied by multiple endocrine neoplasms of the parat...

Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients.

BACKGROUND: Heart failure is common in patients receiving hemodialysis. A high-flow arteriovenous fistula (AVF) may represent a modifiable risk factor...

Using machine learning models for predicting monthly iPTH levels in hemodialysis patients.

BACKGROUND AND OBJECTIVE: Intact parathyroid hormone (iPTH), also known as active parathyroid hormone, is an important indicator commonly for monitori...

Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.

Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes o...

Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.

BACKGROUND: Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatmen...

Prediction of dialysis adequacy using data-driven machine learning algorithms.

BACKGROUND: Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcom...

MultiThal-classifier, a machine learning-based multi-class model for thalassemia diagnosis and classification.

BACKGROUND: The differential diagnosis between iron deficiency anemia (IDA) and thalassemia trait (TT) remains a significant clinical challenge. This ...

Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.

Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using eryth...

Machine learning-derived clinical decision algorithm for the diagnosis of hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism.

PURPOSE: To train and validate machine learning-derived clinical decision algorithm (CDA) for the diagnosis of hyperfunctioning parathyroid glands usi...

Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk f...

Deep Reinforcement Learning for personalized diagnostic decision pathways using Electronic Health Records: A comparative study on anemia and Systemic Lupus Erythematosus.

BACKGROUND: Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. A...

Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

INTRODUCTION: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accura...