AIMC Topic: Machine Learning

Clear Filters Showing 12731 to 12740 of 34417 articles

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.

Scientific reports
Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learn...

A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities.

Scientific data
Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for ...

Link prediction based on spectral analysis.

PloS one
Link prediction in complex network is an important issue in network science. Recently, various structure-based similarity methods have been proposed. Most of algorithms are used to analyze the topology of the network, and to judge whether there is an...

A machine learning approach to assess Sustainable Development Goals food performances: The Italian case.

PloS one
In this study, we introduce an innovative application of clustering algorithms to assess and appraise Italy's alignment with respect to the Sustainable Development Goals (SDGs), focusing on those related to climate change and the agrifood market. Spe...

Artificial intelligence for oral squamous cell carcinoma detection based on oral photographs: A comprehensive literature review.

Cancer medicine
INTRODUCTION: Oral squamous cell carcinoma (OSCC) presents a significant global health challenge. The integration of artificial intelligence (AI) and computer vision holds promise for the early detection of OSCC through the analysis of digitized oral...

FedOSS: Federated Open Set Recognition via Inter-Client Discrepancy and Collaboration.

IEEE transactions on medical imaging
Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale central...

Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

Applied neuropsychology. Child
The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patterns of inattention, hyperactivity, and impulsivity, the etiology of which may involve a variety of ...

iTTCA-MVL: A multi-view learning model based on physicochemical information and sequence statistical information for tumor T cell antigens identification.

Computers in biology and medicine
Immunotherapy is an emerging treatment method aimed at activating the human immune system and relying on its own immune function to kill cancer cells and tumor tissues. It has the advantages of wide applicability and minimal side effects. Effective i...

Application of Machine Learning Techniques to Development of Emergency Medical Rapid Triage Prediction Models in Acute Care.

Computers, informatics, nursing : CIN
Given the critical and complex features of medical emergencies, it is essential to develop models that enable prompt and suitable clinical decision-making based on considerable information. Emergency nurses are responsible for categorizing and priori...

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.

Journal of endocrinological investigation
OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of h...