Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface.

Alzheimer's disease (AD), the most common neurodegenerative disorder world-wide, presents sex-specific differences in its manifestation and progression, necessitating personalized diagnostic approaches. Current procedures are often costly and invasive, lacking consideration of sex-based differences. This study introduces an explainable machine learning (ML) system to predict and differentiate the progression of AD based on sex, using non-invasive, easily collectible predictors such as neuropsychological test scores and sociodemographic data, enabling its application in every day clinical settings.

Frequent internet use is associated with better episodic memory performance.

As the internet is becoming more and more accessible and prevalent, there has been growing interest in determining the effect of internet use on human cognition, particularly memory. However, much less is known about how internet use frequency is related to episodic memory, which refers to the memory of past events as opposed to facts (i.e.

Unravelling the Acute, Chronic and Steroid-Refractory Management of High-Grade Neurological Immune-Related Adverse Events: A Call to Action.

Rare side effects of immune-checkpoint inhibitors (ICIs) are known as neurological immune-related adverse events (n-irAEs). Typically, n-irAEs affect the peripheral nervous system, primarily presenting as myositis, polyradiculoneuropathy, or cranial neuropathy. Less commonly, they impact the central nervous system, resulting in encephalitis, meningitis, or myelitis.

Unraveling sex differences in Parkinson's disease through explainable machine learning.

Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis.