AIMC Topic: Middle Aged

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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model.

PeerJ
BACKGROUND: Tuberculous pleural effusion (TPE) is a prevalent tuberculosis complication, with diagnosis presenting considerable challenges. Timely and precise identification of TPE is vital for effective patient management and prognosis, yet existing...

Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

PloS one
INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MR...

Machine learning model based on survey assessment of sleep quality in chronic obstructive pulmonary disease patients.

PloS one
PURPOSE: The aim is to develop a learning model based on clinical and survey data to assess sleep quality and identify determining factors affecting sleep quality in chronic obstructive pulmonary disease (COPD) patients.

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Depression and anxiety
Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study aims to explore the relationship between CSA and the subjective and objective parameters of sleep in patients with short-term insomnia and construct ma...

Amphetamine use and Parkinson's disease: integration of artificial intelligence prediction, clinical corroboration, and mechanism of action analyses.

PloS one
Parkinson's disease (PD) is an increasingly prevalent neurologic condition for which symptomatic, but not preventative, treatment is available. Drug repurposing is an innovate drug discovery method that uncovers existing therapeutics to treat or prev...

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...

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...