AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age- and sex-matched controls.

Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.

Annals of vascular surgery
BACKGROUND: Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterectomy (CEA). The present work aimed to assess the accuracy of an artificial intelligence (AI)-powered ...

Factors inducing cutaneous adverse reactions in cancer patients treated with PD-1 and PD-L1 inhibitors: a machine-learning algorithm approach.

Immunopharmacology and immunotoxicology
BACKGROUND: Immune checkpoint inhibitors (ICIs) show promise in cancer treatment but can lead to immune-related adverse events (irAEs), notably affecting the skin. Understanding the factors behind these skin reactions is crucial for effective managem...

How do older adults react to social robots' offspring-like voices.

Social science & medicine (1982)
Social robots are being developed as a technological solution to alleviate older adults' loneliness due to separation from their offspring. This study explores how and why offspring-like voices affect older adults' acceptance of social robots from an...

Establishing a machine learning dementia progression prediction model with multiple integrated data.

BMC medical research methodology
OBJECTIVE: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerative dementia are highly valuable. Machine learning (ML) methods facilitate the construction of effec...

Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation.

JMIR medical informatics
BACKGROUND: Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of mor...

Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning-Based Predictive Model.

Journal of medical Internet research
BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently develops into sepsis or even death. Notably, the incidences of SIRS and sepsis steadily increase wi...

Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress.

BMC cancer
BACKGROUND: Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophagea...

Predicting Progression to Dementia Using Auditory Verbal Learning Test in Community-Dwelling Older Adults Based On Machine Learning.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
BACKGROUND: Primary healthcare institutions find identifying individuals with dementia particularly challenging. This study aimed to develop machine learning models for identifying predictive features of older adults with normal cognition to develop ...