AIMC Topic: ROC Curve

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Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.

Scientific reports
Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a fee...

Development and validation of an interpretable predictive machine learning model for successful weaning of continuous renal replacement therapy.

Scientific reports
Continuous renal replacement therapy (CRRT) is a vital intervention for critically ill patients with severe acute kidney injury, yet no standardized criteria exist to determine the optimal time for its discontinuation. We developed and validated mach...

Machine learning classification of inflammatory bowel disease activity using white blood cell subsets.

BMJ open gastroenterology
OBJECTIVE: The lack of a rapid, validated, consistent test for tracking disease activity in patients with inflammatory bowel disease (IBD) is currently a major challenge. Currently used biomarkers have notable disadvantages, such as the slow processi...

Development and validation of nomogram and machine learning models to predict sarcopenia in patients with chronic kidney disease.

Scientific reports
Chronic kidney disease (CKD) is a growing public health problem worldwide. CKD not only leads to renal function decline but also increases the risk of multiple complications, sarcopenia being particularly common and severe. At present, there is a lac...

Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study.

Journal of clinical microbiology
UNLABELLED: Matrix-assisted laser desorption-ionization-time of flight (MALDI-TOF) mass spectra can be used to predict antimicrobial resistance (AMR) using machine learning (ML). This study aimed to validate the performance of ML models for AMR predi...

Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning.

BMC medical imaging
BACKGROUND: Radiomics and artificial intelligence have shown strong predictive capabilities in urinary stone research, particularly concerning stone composition, characteristics, and treatment outcomes. However, the association of stone radiomics and...

Explainable ensemble learning for Epstein-Barr virus risk prediction in ulcerative colitis and Crohn's disease using routine biomarkers.

Scientific reports
Epstein-Barr virus (EBV) exacerbates inflammatory bowel disease (IBD) and is challenging to monitor with invasive or costly tests. We investigated whether explainable machine learning can predict EBV infection from routine clinical data in ulcerative...

Development and validation of a machine learning model to predict early recurrence after surgery in NSCLC patients.

Scientific reports
To develop and validate a machine learning (ML) model for predicting early recurrence (ER) within two years post-surgery in non-small cell lung cancer (NSCLC) patients. This multicenter cohort study included 3,171 NSCLC patients who underwent radical...

Research on the prediction of slow blood flow in pPCI of STEMI patients based on CatBoost.

European journal of medical research
BACKGROUND: In recent years, the incidence of ST-segment elevation myocardial infarction (STEMI) has been on the rise, leading to an increase in the number of patients undergoing direct percutaneous coronary intervention (pPCI). However, some patient...