Urology

Rx-LLM: a benchmarking suite to evaluate safe large language model performance for medication-related tasks

For large language models (LLMs) to reach their potential as information technology tools that make medication use safer, clinically relevant benchmarks capable of automated grading and designed specifically to measure the performance of LLMs for medication tasks are required. The purpose of this study was to design a suite of benchmarking tests reflective of Comprehensive Medication Management (C...

Machine Learning for Urinary Tract Infection Prediction in Emergency Departments: An Explainable Approach

Urinary tract infections (UTIs) represent a substantial burden in emergency department (ED) settings, where diagnostic delays and the limitations of traditional clinical assessments often result in suboptimal treatment decisions. This study develops an interpretable machine learning framework to enhance real-time UTI prediction accuracy. We analyzed a retrospective dataset of 80,387 ED patient enc...

A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.

BACKGROUND: Extremely aggressive prostate cancer, including subtypes like small cell carcinoma and neuroendocrine carcinoma, is associated with poor p...

Providing a Prostate Cancer Detection and Prevention Method With Developed Deep Learning Approach.

Prostate cancer is the second most common cancer among men worldwide. This cancer has become extremely noticeable due to the increase of prostate can...

Machine learning with decision curve analysis evaluates nutritional metabolic biomarkers for cardiovascular-kidney-metabolic risk: an NHANES analysis.

BACKGROUND: The American Heart Association recently introduced the concept of Cardiovascular-Kidney-Metabolic Syndrome (CKM), emphasizing the interpla...

Predicting in-hospital mortality in patients with alcoholic cirrhosis complicated by severe acute kidney injury: development and validation of an explainable machine learning model.

BACKGROUND: At present, there are no specialized models for predicting mortality risk in patients with alcoholic cirrhosis complicated by severe acute...

Autoencoder techniques for survival analysis on renal cell carcinoma.

Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of...

Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of ...

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.

Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperati...

Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach.

BACKGROUND: Prostate cancer is a common malignancy in men, and accurately distinguishing between benign and malignant nodules at an early stage is cru...

Modeling protective meningococcal antibody responses and factors influencing antibody persistence following vaccination with MenAfriVac using machine learning.

Meningococcal meningitis poses a significant public health burden in the meningitis belt region of sub-Saharan Africa. The introduction of the meningo...

RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.

Purpose To evaluate the performance of the winning machine learning models from the 2023 RSNA Abdominal Trauma Detection AI Challenge. Materials and M...

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 ...

Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.

Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using pat...

Artificial Intelligence to Predict Chronic Kidney Disease Progression to Kidney Failure: A Narrative Review.

Chronic kidney disease is characterised by the progressive loss of kidney function. However, predicting who will progress to kidney failure is difficu...

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and c...

Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.

Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been...

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type...

Current and Future Applications of Artificial Intelligence to Diagnose and Treat Male Infertility.

Artificial intelligence (AI) models are being increasingly applied to modern medicine. Within the field of urology, reproductive urology specifically ...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to ...