Primary Care

Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals from the general population, the screening targets can be narrowed to individuals who are in most need of an endoscopy. This study was designed to devel...

Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach.

Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associated diseases are required for treatment. 34 KEGG pathways targeted by lipid lowering drugs were used to construct a directed protein-protein interaction network and driver nodes were determined using CytoCtrlAnalyser plugin of Cytoscape 3.6. The inv...

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.

Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data re...

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-lear...

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation.

Background The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-...

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.

INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study ...

Efficient Automated Disease Diagnosis Using Machine Learning Models.

Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease ma...

Comparison of short- and long-term postoperative occurrences after robotic single-incision cholecystectomy versus multiport laparoscopic cholecystectomy.

BACKGROUND: Long-term outcomes of SIRC are not well established. Furthermore, SIRC is only now being considered more frequently for patients with inde...

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

IMPORTANCE: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high-...

Q-CHAT-NAO: A robotic approach to autism screening in toddlers.

The use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results w...

Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network.

Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutiona...

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.

BACKGROUND: Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate....

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. ...

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical condit...

Challenges and opportunities for artificial intelligence in oncological imaging.

Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, ...

Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning.

BACKGROUND: Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). Ho...

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.

BACKGROUND: Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or scre...

Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh.

BACKGROUND AND AIMS: Hypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due...

Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology.

In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to tr...

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods.

BACKGROUND: Genotype-phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variation...