STDs

STI/HIV risk prediction model development-A novel use of public data to forecast STIs/HIV risk for men who have sex with men.

A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG Boost are applied and evaluated on the Demographic and Health Surveys Program (DHSP), with thirteen features ultimately selected as the most predicti...

CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer.

BACKGROUND: Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk stratification.

Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.

BACKGROUND: A significant proportion of individuals with symptoms of sexually transmitted infection (STI) delay or avoid seeking healthcare, and digit...

Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data.

Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance i...

Robust modelling of arterial blood pressure reconstruction from photoplethysmography.

Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) waveforms contain information that reflects the car...

Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques.

HIV remains a critical global health issue, with an estimated 39.9 million people living with the virus worldwide by the end of 2023 (according to WHO...

anti-cancer activity of siddha metallo-mineral formulation from Thanga uram with Hela cell lines.

The second most common malignant tumour in women worldwide, cervical cancer seriously jeopardizes their health. It has been established that a high-ri...

A lightweight deep learning method to identify different types of cervical cancer.

Cervical cancer is the second most common cancer in women's bodies after breast cancer. Cervical cancer develops from dysplasia or cervical intraepith...

The use of machine learning to understand the role of visual attention in multi-attribute choice.

Whether eye movements (as a measure of visual attention) contribute to the understanding of how multi-attribute decisions are made, is still a matter ...

Ethical and pedagogical implications of AI in language education: An empirical study at Ha'il University.

This study aims to evaluate the role of AI as an educational tool from an ethical and pedagogical perspective as it delves into the perceptions of the...

Testing process for artificial intelligence applications in radiology practice.

Artificial intelligence (AI) applications are becoming increasingly common in radiology. However, ensuring reliable operation and expected clinical be...

Improving tabular data extraction in scanned laboratory reports using deep learning models.

OBJECTIVE: Medical laboratory testing is essential in healthcare, providing crucial data for diagnosis and treatment. Nevertheless, patients' lab test...

Peripheral Blood Mononuclear Cell Biomarkers for Major Depressive Disorder: A Transcriptomic Approach.

Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fa...

Diagnostic performance of an automated robot for MALDI target preparation in microbial identification.

The MBT Pathfinder is an automated colony-picking robot designed for efficient sample preparation in matrix-assisted laser desorption/ionization time-...

Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.

One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and pla...

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the i...

Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study.

PURPOSE: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening ...

Combination therapy synergism prediction for virus treatment using machine learning models.

Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on compu...

TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.

The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge...

Optimal STI controls for HIV patients based on an efficient deep Q learning method.

We investigate an efficient computational tool to suggest useful treatment regimens for people infected with the human immunodeficiency virus (HIV). S...