AIMC Topic: ROC Curve

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Development and validation of an explainable machine learning model for predicting sepsis risk following flexible ureteroscopic lithotripsy.

Urolithiasis
Sepsis is a severe complication of flexible ureteroscopic lithotripsy (fURL), a widely used treatment for kidney stones. This study aimed to develop and validate a predictive model based on machine learning (ML) for assessing the risk of sepsis follo...

Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study.

JMIR medical informatics
BACKGROUND: Bipolar disorder (BD) is a highly recurrent disorder. Early detection, early intervention, and prevention of recurrent bipolar mood symptoms are key to a better prognosis.

Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes.

PloS one
BACKGROUND: Motor proteins play significant roles in cancer progression, but their involvement in biochemical recurrence (BCR) of prostate cancer remains unclear. The objective of the study is to develop a prognostic indicator for BCR using machine l...

Machine learning-based prediction model for 28-day mortality in acute kidney injury patients with liver cirrhosis: A MIMIC-IV database analysis.

PloS one
BACKGROUND: Acute kidney injury (AKI) in patients with liver cirrhosis represents a significant clinical challenge with high mortality rates. This study aimed to develop and validate a machine learning-based prediction model for 28-day mortality in A...

Benchmarking feature projection methods in radiomics.

Scientific reports
In radiomics, feature selection methods are primarily used to eliminate redundant features and identify relevant ones. Feature projection methods, such as principal component analysis (PCA), are often avoided due to concerns that recombining features...

Machine learning predictions of unplanned readmissions using electronic medical records: Predictor importance across medical and surgical patient populations.

PloS one
Hospital readmissions prolong patient suffering and increase healthcare expenditures. While several studies have attempted to develop prediction models to reduce readmissions, most have demonstrated modest predictive accuracy. To improve upon prior a...

Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts.

PloS one
PURPOSE: To investigate the effectiveness of an integrated deep-learning (DL) algorithm, the Mixture of Radiological Findings Specific Experts (MoRFSE), in breast cancer classification by imitating the diagnostic decision-making process of radiologis...

Artificial intelligence approach to optimise safety for hospitalised patients with dementia.

BMJ open quality
BACKGROUND: The aim of the study is to develop a machine learning (ML) model to identify contributing factors to dementia-related safety events using patient safety event report data.

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation.

Annals of medicine
BACKGROUND: Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI...