Primary Care

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.

Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in large...

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis co...

Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health.

In Low- and Middle- Income Countries (LMICs), machine learning (ML) and artificial intelligence (AI) offer attractive solutions to address the shortag...

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed netwo...

Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives.

According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly...

Machine Learning Algorithms for Predicting Fatty Liver Disease.

BACKGROUND: Fatty liver disease (FLD) has become a rampant condition. It is associated with a high rate of morbidity and mortality in a population. Th...

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-targe...

A Novel Automated Screening Method for Combinatorially Generated Small Molecules.

A main challenge in the enumeration of small-molecule chemical spaces for drug design is to quickly and accurately differentiate between possible and ...

Predicting women with depressive symptoms postpartum with machine learning methods.

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's hea...

Leveraging data and AI to deliver on the promise of digital health.

Rising rates of NCDs threaten fragile healthcare systems in low- and middle-income countries. Fortunately, new digital technology provides tools to mo...

Does Physical Activity Predict Obesity-A Machine Learning and Statistical Method-Based Analysis.

BACKGROUND: Obesity prevalence has become one of the most prominent issues in global public health. Physical activity has been recognized as a key pla...

Robot-assisted laparoscopic surgery combined with axitinib for renal cell carcinoma and inferior vena cava tumor thrombi in a patient with severe obesity and obstructive sleep apnea-hypopnea syndrome: A case report.

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly.

Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research effo...

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews.

BACKGROUND: Systematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature i...

Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction.

Postoperative cognitive dysfunction (POCD) refers to the complications of the central nervous system before and after surgery in patients without ment...

Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study.

BACKGROUND: In recent years, mobile-based interventions have received more attention as an alternative to on-site obesity management. Despite increase...

Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan.

BACKGROUND: In hospitals, nurses are responsible for pressure injury risk assessment using several kinds of risk assessment scales. However, their pre...

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.

Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of ...

Two novel potent ACEI peptides isolated from meat hydrolysates using analysis: identification, screening and inhibitory mechanisms.

The aim of this study was to discover potent angiotensin-converting enzyme (ACE) inhibitory (ACEI) peptides from () for treating hypertension and to ...

Lightweight pyramid network with spatial attention mechanism for accurate retinal vessel segmentation.

PURPOSE: The morphological characteristics of retinal vessels are vital for the early diagnosis of pathological diseases such as diabetes and hyperten...