AIMC Topic: Middle Aged

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Predicting benign prostatic hyperplasia risks: model development and external validation based on three cohorts.

Global health research and policy
BACKGROUND: As benign prostatic hyperplasia (BPH) becomes increasingly prevalent, there is a growing need for simple and accurate methods to predict its risk. This study aimed to develop and validate a prediction model to identify males at high risk ...

[Formula: see text] : explainable attentive transformers for identifying the factors influencing dental visits to enhance dental data completeness.

BMC oral health
BACKGROUND: Access to routine dental care is a cornerstone of preventive healthcare. Regular dental check-ups, which include professional cleanings, examinations, and preventive treatments, play a crucial role in preventing advanced dental diseases s...

Site-specific pain dynamics: associations between accelerometer-measured physical activity patterns and pain in older adults.

The journal of headache and pain
BACKGROUND: Physical activity (PA) has emerged as a promising non-pharmacological intervention for pain management, the relationship between objectively measured PA patterns and multi-site pain remains poorly understood. This exploratory study invest...

Mixture of checkpoint experts for explainable seizure detection using wearable devices.

Scientific reports
The current gold standard for detecting epileptic seizures is in-hospital video-Electroencephalography (vEEG), but vEEG is resource-intensive and imposes considerable burdens on patients and caregivers. Wearable devices offer an alternative to monito...

Muscle synergy-driven ensemble learning framework for individualized stroke gait rehabilitation.

Scientific reports
This study proposes a novel ensemble machine learning (ML) framework integrating neurophysiological principles from muscle synergy analysis to support clinical decisions in stroke gait rehabilitation. The framework leverages spatial and temporal feat...

Machine learning-based prediction of stone-free status following extracorporeal shock wave lithotripsy.

World journal of urology
PURPOSE: To develop a machine learning model for predicting stone-free (SF) outcomes following extracorporeal shock wave lithotripsy (SWL) and to identify key clinical and stone-related predictors using interpretable machine learning techniques.

Real-world evaluation of the accuracy of the Viz.AI automated intracranial hemorrhage volume calculation tool.

Journal of neurointerventional surgery
BACKGROUND: Appropriate management of spontaneous intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) requires rapid, accurate volume estimation. Viz.AI has developed an artificial intelligence (AI)-powered ICH calculation tool that ...

Age-dependent effects of surgical approach in T3b differentiated thyroid carcinoma: a population-based analysis using machine learning.

Endocrine-related cancer
Current guidelines recommend total thyroidectomy for all T3b differentiated thyroid carcinoma (DTC) with gross strap muscle invasion, yet evidence supporting this universal approach remains limited and conflicting. We analyzed 6,920 T3b DTC patients ...

Identifying daily-living features related to loneliness: A causal machine learning approach.

PloS one
BACKGROUND: Loneliness is a distressing feeling that influences well-being. Immigrants' experience of acculturation to a new dominant culture places them at risk for maladaptive behaviors and daily rhythms leading to loneliness. Identifying daily-liv...

Biological age threshold is associated with symptomatic knee osteoarthritis risk in chinese adults: Insights from machine learning analysis of a national cohort.

PloS one
BACKGROUND: Symptomatic knee osteoarthritis (KOA) imposes a substantial global health and economic burden. Although chronological age (CA) is a key risk factor, it poorly reflects interindividual aging heterogeneity. Biological age (BA), which is qua...