AIMC Topic: Sexually Transmitted Diseases

Clear Filters Showing 11 to 17 of 17 articles

Development of a Novel Fluorescent-Based Lateral Flow Assay for the Detection of Neisseria gonorrhoeae at the Point of Care.

Sexually transmitted diseases
BACKGROUND: Neisseria gonorrhoeae (NG) has acquired significant resistance, primarily due to extensive and unwarranted antibiotic utilization over several decades. This resistance has largely been associated with the syndromic management of sexually ...

Using Machine Learning to Identify Predictors of Sexually Transmitted Infections Over Time Among Young People Living With or at Risk for HIV Who Participated in ATN Protocols 147, 148, and 149.

Sexually transmitted diseases
BACKGROUND: Sexually transmitted infections (STIs) among youth aged 12 to 24 years have doubled in the last 13 years, accounting for 50% of STIs nationally. We need to identify predictors of STI among youth in urban HIV epicenters.

Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier.

Journal of medical systems
Cervical cancer is the fourth most communal malignant disease amongst women worldwide. In maximum circumstances, cervical cancer indications are not perceptible at its initial stages. There are a proportion of features that intensify the threat of em...

Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
BACKGROUND: Machine learning (ML) shows promise for sexually transmitted infection (STI) risk prediction, but systematic evidence of its effectiveness remains fragmented.

Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of t...

Adapting an artificial intelligence sexually transmitted diseases symptom checker tool for Mpox detection: the HeHealth experience.

Sexual health
Artificial Intelligence (AI) applications have shown promise in the management of pandemics. In response to the global Monkeypox (Mpox) outbreak, the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases (STD) to dev...