AIMC Topic: ROC Curve

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Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.

PloS one
The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy, diagnosis codes aimed at billing should not be directly used for clinical research because of the r...

A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.

PloS one
BACKGROUND: Acinetobacter baumanni infection is a leading cause of morbidity and mortality in the Intensive Care Unit (ICU). Early recognition of patients at risk for infection allows early proper treatment and is associated with improved outcomes. T...

Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy.

Cell reports. Medicine
Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framew...

Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.

BMC pregnancy and childbirth
BACKGROUND: Preterm birth (PTB) is a significant cause of neonatal mortality and long-term health issues. Accurate prediction and timely prevention of PTB are essential for reducing associated child mortality and morbidity. Traditional predictive met...

Detection of basal cell carcinoma by machine learning-assisted ex vivo confocal laser scanning microscopy.

International journal of dermatology
BACKGROUND: Ex vivo confocal laser scanning microscopy (EVCM) is an emerging imaging modality that enables near real-time histology of whole tissue samples. However, the adoption of EVCM into clinical routine is partly limited because the recognition...

The use of cloud based machine learning to predict outcome in intracerebral haemorrhage without explicit programming expertise.

Neurosurgical review
Machine Learning (ML) techniques require novel computer programming skills along with clinical domain knowledge to produce a useful model. We demonstrate the use of a cloud-based ML tool that does not require any programming expertise to develop, val...

Development of machine learning-based models to predict congenital heart disease: A matched case-control study.

International journal of medical informatics
BACKGROUND: The current congenital heart disease (CHD) prediction tools lack adequate interpretability and convenience, hindering the development of personalized CHD management strategies. We developed a machine learning-based risk stratification mod...

Comparison of deep learning schemes in grading non-alcoholic fatty liver disease using B-mode ultrasound hepatorenal window images with liver biopsy as the gold standard.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND/INTRODUCTION: To evaluate the performance of pre-trained deep learning schemes (DLS) in hepatic steatosis (HS) grading of Non-Alcoholic Fatty Liver Disease (NAFLD) patients, using as input B-mode US images containing right kidney (RK) cort...