Primary Care

Automatic assessment of Alzheimer's disease diagnosis based on deep learning techniques.

Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonan...

Artificial Intelligence and Digital Tools: Future of Diabetes Care.

Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabetes mellitus. Diabetes represents a trove of physiologic and sociologic data that are only superficially understood by the health care system. Artificial intelligence can address many problems posed by the prevalence of diabetes mellitus and the impa...

Selected Papers from 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (IEEE ECBIOS 2019).

Recently, healthcare has undergone a sector-wide transformation thanks to advances in computing, networking technologies, big data, and artificial int...

Contemporary Rates and Predictors of Open Conversion During Minimally Invasive Radical Prostatectomy for Nonmetastatic Prostate Cancer.

To test contemporary rates and predictors of open conversion at minimally invasive (laparoscopic or robotic) radical prostatectomy (MIRP). Within th...

Systems metabolomics: from metabolomic snapshots to design principles.

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutr...

Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods.

OBJECTIVE: The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in...

Machine Learning in oncology: A clinical appraisal.

Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but autom...

Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer.

BACKGROUND: Improving the speed of systematic review (SR) development is key to supporting evidence-based medicine. Machine learning tools which semi-...

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome.

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requ...

Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis: a case-control development and validation study.

BACKGROUND: Many individuals who will experience a first episode of psychosis (FEP) are not detected before occurrence, limiting the effect of prevent...

Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks.

BACKGROUND: In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to...

SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network.

Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-dise...

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAP).

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat di...

Star Trek, medicine, ethics, nanotechnology and nursing.

The first collection of Star Trek (ST) papers in this journal concentrated on the various doctors that appeared in ST in chronological screening order...

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to...

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (...

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

BACKGROUND: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time...

Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing coun...

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when r...

Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population.

Hypertension is the leading risk factor of cardiovascular disease and has profound effects on both the structure and function of the microvasculature....