AIMC Topic: Area Under Curve

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Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Machine learning and deep learning models are very powerful in predicting the presence of a disease. To achieve good predictions, those models require a certain amount of data to train on, whereas this amount i) is generally...

Safety, tolerability, pharmacokinetics, and pharmacodynamics of oral JNJ-64794964, a TLR-7 agonist, in healthy adults.

Antiviral therapy
BACKGROUND: This Phase I, two-part, first-in-human study assessed safety/tolerability and pharmacokinetics/pharmacodynamics of single-ascending doses (SAD) and multiple doses (MD) of the oral toll-like receptor-7 agonist, JNJ-64794964 (JNJ-4964) in h...

Roza: a new and comprehensive metric for evaluating classification systems.

Computer methods in biomechanics and biomedical engineering
Many metrics such as accuracy rate (ACC), area under curve (AUC), Jaccard index (JI), and Cohen's kappa coefficient are available to measure the success of the system in pattern recognition and machine/deep learning systems. However, the superiority ...

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR.

Journal of translational medicine
BACKGROUND: This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast le...

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Computational and mathematical methods in medicine
Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pa...

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.

The Lancet. Digital health
BACKGROUND: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pa...

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

EBioMedicine
BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.

Deep embeddings and logistic regression for rapid active learning in histopathological images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recognizing different tissue components is one of the most fundamental and essential works in digital pathology. Current methods are often based on convolutional neural networks (CNNs), which need numerous annotated samples ...

Deep learning on fundus images detects glaucoma beyond the optic disc.

Scientific reports
Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma dete...