Primary Care

Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury.

Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is associated with secondary brain injury, and both intensity and duration of an episode of intracranial hypertension, often referred to as "ICP dose," are associated with worse outcomes. Prediction of such harmful episodes of ICP dose could allow for a mor...

Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population.

PURPOSE: Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for screening primary osteopenia and osteoporosis based on the lumbar radiographs, and to compare the diagnostic performance of the CNN model adding the clinical covariates with the image model alone.

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.

We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross...

The Influence of Public Mental Health Based on Artificial Intelligence Technology on the Teaching Effect of Business Administration Major.

As an emerging technology, public mental health based on artificial intelligence engineering has broad application and development prospects in improv...

Alternative stopping rules to limit tree expansion for random forest models.

Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, an...

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust compar...

Relationship between Brachial-Ankle Pulse Wave Velocity and Fundus Arteriolar Area Calculated Using a Deep-Learning Algorithm.

PURPOSE: Retinal vessels reflect alterations related to hypertension and arteriosclerosis in the physical status. Previously, we had reported a deep-l...

Improvement of intervention information detection for automated clinical literature screening during systematic review.

Systematic literature review (SLR) is a crucial method for clinicians and policymakers to make their decisions in a flood of new clinical studies. Bec...

Novel Internet of Things based approach toward diabetes prediction using deep learning models.

The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medi...

Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders.

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This...

Atrous residual convolutional neural network based on U-Net for retinal vessel segmentation.

Extracting features of retinal vessels from fundus images plays an essential role in computer-aided diagnosis of diseases, such as diabetes, hypertens...

Automated diagnosing primary open-angle glaucoma from fundus image by simulating human's grading with deep learning.

Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness worldwide. Although deep learning methods have been proposed to diagno...

Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease.

This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neura...

Deep learning fuzzy immersion and invariance control for type-I diabetes.

In this study, a novel approach is proposed for glucose regulation in type-I diabetes patients. Unlike most studies, the glucose-insulin metabolism is...

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Comp...

Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods.

Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can asses...

Liver enzymes, alcohol consumption and the risk of diabetes: the Suita study.

AIM: We aimed to investigate the combined impact of liver enzymes and alcohol consumption on the diabetes risk.

Nuclear morphology is a deep learning biomarker of cellular senescence.

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack...

Characterization of the SGLT2 Interaction Network and Its Regulation by SGLT2 Inhibitors: A Bioinformatic Analysis.

Sodium-glucose cotransporter 2 (SGLT2), also known as solute carrier family 5 member 2 (SLC5A2), is a promising target for a new class of drugs prima...

A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction.

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpr...