AIMC Topic: Machine Learning

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Machine learning to predict cardiovascular risk.

International journal of clinical practice
AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales.

Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device.

Sensors (Basel, Switzerland)
The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, the...

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

Neuroradiology
PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker whic...

Low-rank analysis-synthesis dictionary learning with adaptively ordinal locality.

Neural networks : the official journal of the International Neural Network Society
Analysis dictionary learning (ADL) has been successfully applied to a variety of learning systems. However, the ordinal locality of analysis dictionary has rarely been explored in constructing discriminative terms. In this paper, a discriminative low...

Artificial Intelligence for Drug Toxicity and Safety.

Trends in pharmacological sciences
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and preve...

Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

JAMA network open
IMPORTANCE: Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerize...

Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.

JAMA network open
IMPORTANCE: Pulmonary embolism (PE) is a life-threatening clinical problem, and computed tomographic imaging is the standard for diagnosis. Clinical decision support rules based on PE risk-scoring models have been developed to compute pretest probabi...

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation.

JAMA network open
IMPORTANCE: Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that...

Learning to Quantify Emphysema Extent: What Labels Do We Need?

IEEE journal of biomedical and health informatics
Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variabi...