AIMC Topic: Female

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SMART (artificial intelligence enabled) DROP (diabetic retinopathy outcomes and pathways): Study protocol for diabetic retinopathy management.

PloS one
INTRODUCTION: Delayed diagnosis of diabetic retinopathy (DR) remains a significant challenge, often leading to preventable blindness and visual impairment. Given that physicians are frequently the first point of contact for people with diabetes, ther...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

PloS one
Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weight gain is augmenting the disease burden and healthcare expenditure. However, predictors of psychotro...

Breast cancer pathology image recognition based on convolutional neural network.

PloS one
This study presents a convolutional neural network (CNN)-based method for the classification and recognition of breast cancer pathology images. It aims to solve the problems existing in traditional pathological tissue analysis methods, such as time-c...

A machine learning approach to predicting postoperative recurrence in pediatric chronic rhinosinusitis: identification of key metabolic biomarkers.

American journal of otolaryngology
BACKGROUND: Pediatric chronic rhinosinusitis (CRS) is a common chronic inflammatory disease with a high recurrence rate after surgery. This study aimed to construct and validate a machine learning-based predictive model to predict the risk of postope...

Developing a smart system for binary classification of disordered voices using machine learning.

American journal of otolaryngology
OBJECTIVES: Voice disorder is characterized by disruptions in voice quality caused by issues in vocal fold vibration during phonation. The study explored the application of machine learning, based on the Random Forest (RF) and Decision Tree (DT) mode...

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.

PloS one
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors ca...

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

PloS one
BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles a...

Professional identity and its relationships with AI readiness and interprofessional collaboration.

PloS one
BACKGROUND: In contemporary healthcare practices, the convergence of Artificial Intelligence (AI) and interprofessional collaboration represents a transformative era marked by unprecedented opportunities and challenges. The introduction of AI technol...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

Clinical psychology & psychotherapy
This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-sectional sample of 368 ASD-diagnosed young people (aged 18-24 years) was recruited, and 34 candidate predi...

Modeling protective meningococcal antibody responses and factors influencing antibody persistence following vaccination with MenAfriVac using machine learning.

PloS one
Meningococcal meningitis poses a significant public health burden in the meningitis belt region of sub-Saharan Africa. The introduction of the meningococcal PsA-TT vaccine (MenAfriVac®) has successfully eliminated Neisseria meningitidis serogroup A (...