AIMC Topic: Algorithms

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Machine learning algorithms for heart disease diagnosis: A systematic review.

Current problems in cardiology
BACKGROUND: The heart is a vital organ that pumps blood throughout the body. Its proper functioning is crucial for maintaining overall health, and any malfunction can significantly impact other bodily systems. Recently, machine learning has emerged a...

Development of machine learning models for estimation of disintegration time on fast-disintegrating tablets.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
The disintegration time for solid dosage oral formulations is directly influenced by diverse factors such as molecular properties, physical characteristics, excipient compositions, and formulation-specific attributes. This research addresses the chal...

Understanding deep learning models for Length of Stay prediction on critically ill patients through latent space visualization.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Continuous, real-time monitoring of Length of Stay (LoS) for critically ill patients in Intensive Care Units (ICUs) is essential for anticipating patient needs, reduce the risk of adverse events, optimize resource allocation...

Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024).

Computer methods and programs in biomedicine
BACKGROUND: Coronary artery disease (CAD) is the most common cardiovascular disease, exacting high morbidity and mortality worldwide. CAD is detected on coronary artery imaging; coronary artery segmentation (CAS) of the images is essential for corona...

ADEPT: An advanced data exploration and processing tool for clinical data insights.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The rapid growth of clinical data creates challenges in analysis and interpretation for medical professionals. To address these issues, we developed the Advanced Data Exploration and Processing Tool (ADEPT), integrating data...

Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.

Molecular diversity
MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potentia...

Fully automated evaluation of condylar remodeling after orthognathic surgery in skeletal class II patients using deep learning and landmarks.

Journal of dentistry
OBJECTIVE: Condylar remodeling is a key prognostic indicator in maxillofacial surgery for skeletal class II patients. This study aimed to develop and validate a fully automated method leveraging landmark-guided segmentation and registration for effic...

Assessing real-life food consumption in hospital with an automatic image recognition device: A pilot study.

Clinical nutrition ESPEN
BACKGROUND AND AIMS: Accurate dietary intake assessment is essential for nutritional care in hospitals, yet it is time-consuming for caregivers and therefore not routinely performed. Recent advancements in artificial intelligence (AI) offer promising...

MT-RCAF: A Multi-Task Residual Cross Attention Framework for EEG-based emotion recognition and mood disorder detection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Prolonged abnormal emotions can gradually evolve into mood disorders such as anxiety and depression, making it critical to study the relationship between emotions and mood disorders to explore the causes of mood disorders. E...