AIMC Topic: Machine Learning

Clear Filters Showing 14021 to 14030 of 34417 articles

What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation.

Journal of medical Internet research
BACKGROUND: There is increasing interest in the use of artificial intelligence (AI) in pathology to increase accuracy and efficiency. To date, studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting the need for fu...

DeepCancerMap: A versatile deep learning platform for target- and cell-based anticancer drug discovery.

European journal of medicinal chemistry
Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensiv...

Demystifying evidential Dempster Shafer-based CNN architecture for fetal plane detection from 2D ultrasound images leveraging fuzzy-contrast enhancement and explainable AI.

Ultrasonics
Ultrasound imaging is a valuable tool for assessing the development of the fetal during pregnancy. However, interpreting ultrasound images manually can be time-consuming and subject to variability. Automated image categorization using machine learnin...

Artificial Intelligence Applications in Hepatology.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine...

A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges.

Sensors (Basel, Switzerland)
Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine l...

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms.

BMC medical research methodology
BACKGROUND: Trauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the IC...

Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction.

The Laryngoscope
BACKGROUND: Machine learning (ML) analysis of biometric data in non-controlled environments is underexplored.

Machine Learning Model for Assessment of Risk Factors and Postoperative Day for Superficial vs Deep/Organ-Space Surgical Site Infections.

Surgical innovation
Deep and organ space surgical site infections (SSI) require more intensive treatment, may result in more severe clinical disease and may have different risk factors when compared to superficial SSIs. Machine learning (ML) algorithms provide the oppo...

Novel machine learning method allerStat identifies statistically significant allergen-specific patterns in protein sequences.

The Journal of biological chemistry
Cutting-edge technologies such as genome editing and synthetic biology allow us to produce novel foods and functional proteins. However, their toxicity and allergenicity must be accurately evaluated. It is known that specific amino acid sequences in ...

XAI-enabled neural network analysis of metabolite spatial distributions.

Analytical and bioanalytical chemistry
We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly i...