AIMC Topic: Machine Learning

Clear Filters Showing 5681 to 5690 of 34417 articles

Development and application of a machine learning-based antenatal depression prediction model.

Journal of affective disorders
BACKGROUND: Antenatal depression (AND), occurring during pregnancy, is associated with severe outcomes. However, there is a lack of objective and universally applicable prediction methods for AND in clinical practice. We leveraged sociodemographic an...

Large language models vs human for classifying clinical documents.

International journal of medical informatics
BACKGROUND: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and...

Automated model discovery for tensional homeostasis: Constitutive machine learning in growth and remodeling.

Computers in biology and medicine
We present a built-in physics neural network architecture, known as inelastic Constitutive Artificial Neural Network (iCANN), to discover the inelastic phenomenon of tensional homeostasis. In this course, identifying the optimal model and material pa...

Using machine learning to predict selenium content in crops: Implications for soil health and agricultural land utilization in longevity regions.

The Science of the total environment
Selenium (Se) is an indispensable trace element to human health, yet its biological tolerance threshold is relatively narrow. The potential application of machine learning methods to indirectly predict the Se content in crops across regional areas, t...

Assessing foraging landscape quality in Quebec's commercial beekeeping through remote sensing, machine learning, and survival analysis.

Journal of environmental management
Honey bees (Apis mellifera) play an important role in our agricultural systems. In recent years, beekeepers have reported high colony mortality rates in several parts of the world. Inadequate foraging landscapes are often cited as a major factor dete...

Unpaired Dual-Modal Image Complementation Learning for Single-Modal Medical Image Segmentation.

IEEE transactions on bio-medical engineering
OBJECTIVE: Multi-modal MR/CT image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to acquire aligned multi-modal images of a patient in clinical practice due to the high cost and specific allergic re...

Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods.

Sensors (Basel, Switzerland)
In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performanc...

Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis.

Open heart
BACKGROUND: Accurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making and periprocedural planning. Surgical risk models have demonstrated modest discriminative valu...

Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol.

BMJ open
INTRODUCTION: Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early ide...