Primary Care

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography.

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photopl...

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.

Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual expressions in a new cell type or tissue, although it is well known that drug responses depend on a cellular context. Thus, the existing approach has...

Noncirrhotic Portal Hypertension after Trastuzumab Emtansine in HER2-positive Breast Cancer as Determined by Deep Learning-measured Spleen Volume at CT.

Background Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for use in human epidermal growth factor receptor 2 (HER2)-positive br...

The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning.

In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into...

A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.

BACKGROUND: Diabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initi...

Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity.

Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multi...

Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders.

The estimation of defects positioning occurring in the interface between different materials is performed by using an artificial neural network modele...

Assessment of Physical Fitness and Health Status of Athletes Based on Intelligent Medical Treatment under Machine Learning.

Machine learning is an interdisciplinary study of how to make computer programs perform similar to human learning, and its techniques are widely used ...

Comparative Study on the Effect of Various Aerobic Exercises on College Students' Weight Loss Based on Deep Learning Analysis.

In order to solve the problem of higher obesity rate of college students and meet the needs of college students to lose weight effectively, a comparat...

Biomacromolecule-Assisted Screening for Reaction Discovery and Catalyst Optimization.

Reaction discovery and catalyst screening lie at the heart of synthetic organic chemistry. While there are efforts at catalyst design using computati...

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

Fitness is important in people's lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effecti...

The proposed hybrid deep learning intrusion prediction IoT (HDLIP-IoT) framework.

Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are in...

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare.

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and effi...

In-depth evaluation of machine learning methods for semi-automating article screening in a systematic review of mechanistic literature.

We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic re...

Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks.

The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from ...

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification.

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images i...

Determining the effective factors in predicting diet adherence using an intelligent model.

Adhering to a healthy diet plays an essential role in preventing many nutrition-related diseases, such as obesity, diabetes, high blood pressure, and ...

Machine learning-assisted non-target analysis of a highly complex mixture of possible toxic unsymmetrical dimethylhydrazine transformation products with chromatography-mass spectrometry.

Unsymmetrical dimethylhydrazine (UDMH) is a toxic and environmentally hostile compound that was massively introduced to the environment during previou...

Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation.

BACKGROUND: Diabetes is a public health problem worldwide. Although diabetes is a chronic and incurable disease, measures and treatments can be taken ...

Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries.

The aim of this paper is to identify the barriers that are specifically relevant to the use of Artificial Intelligence (AI)-based evidence in Central ...