AIMC Topic: Machine Learning

Clear Filters Showing 11141 to 11150 of 34417 articles

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment.

Scientific reports
This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in the medical and aesthetic fields. We explore the potential of cavitation to alter abdominal fat com...

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water.

Biomedical physics & engineering express
Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological...

Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods.

Lab on a chip
The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening meth...

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

Frontiers in public health
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and mul...

Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.

mAbs
Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten...

A machine learning approach to detect potentially harmful and protective suicide-related content in broadcast media.

PloS one
Suicide-related media content has preventive or harmful effects depending on the specific content. Proactive media screening for suicide prevention is hampered by the scarcity of machine learning approaches to detect specific characteristics in news ...

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning.

PloS one
To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combin...

Machine learning for predicting cognitive deficits using auditory and demographic factors.

PloS one
IMPORTANCE: Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early in...

Advancing intrauterine adhesion severity prediction: Integrative machine learning approach with hysteroscopic cold knife system, clinical characteristics and hematological parameters.

Computers in biology and medicine
Intrauterine Adhesion (IUA) constitute a significant determinant impacting female fertility, potentially leading to infertility, miscarriage, menstrual irregularities, and placental complications. The precise assessment of the severity of IUA is pivo...