AIMC Topic: Databases, Factual

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Healthcare data integration using machine learning: A case study evaluation with health information-seeking behavior databases.

Research in social & administrative pharmacy : RSAP
BACKGROUND: The amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be ...

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection.

IEEE/ACM transactions on computational biology and bioinformatics
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's ur...

Active Fine-Tuning From gMAD Examples Improves Blind Image Quality Assessment.

IEEE transactions on pattern analysis and machine intelligence
The research in image quality assessment (IQA) has a long history, and significant progress has been made by leveraging recent advances in deep neural networks (DNNs). Despite high correlation numbers on existing IQA datasets, DNN-based models may be...

AsthmaKGxE: An asthma-environment interaction knowledge graph leveraging public databases and scientific literature.

Computers in biology and medicine
MOTIVATION: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathop...

Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network.

Sensors (Basel, Switzerland)
Fitness is important in people's lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yog...

Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks.

Sensors (Basel, Switzerland)
Due to wearables' popularity, human activity recognition (HAR) plays a significant role in people's routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: sub...

Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.

Journal of agricultural and food chemistry
The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such c...

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Computational intelligence and neuroscience
Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressu...

Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network.

Artificial intelligence in medicine
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutiona...

Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses.

Artificial intelligence in medicine
BACKGROUND: Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic healt...