AIMC Topic: Age Factors

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Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This scoping review explores how machine learning (ML) and natural language processing (NLP) are used to detect, characterize, and predict neurocognitive symptoms in cancer survivors across age groups. The review had two goals: (1) to compar...

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 ...

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...

Uncovering age-related differences in communication by people with persistent pain when interacting with a pain history assessment chatbot in Australia: an exploratory mixed-methods study using a comparative analysis.

BMJ open
OBJECTIVES: There is limited research exploring the age-related difference in communication when describing pain experiences. This project aimed to identify key differences between adolescents', young adults' and adults' (i) preferred communication m...

Mammo-AGE: deep learning estimation of breast age from mammograms.

Nature communications
Biological age is an important indicator of organ functions and health. Although mammograms are widely used in breast cancer screening, the potential of mammogram-based biological age predictors remains underexplored. Here, we propose a deep learning...

Physiological Response in Children with Autism Spectrum Disorder (ASD) During Social Robot Interaction.

International journal of neural systems
In a world where social interaction presents challenges for children with Autism Spectrum Disorder (ASD), robots are stepping in as allies in emotional learning. This study examined how affective interactions with a humanoid robot elicited physiologi...

Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional Study With AI-Based Analysis.

JMIR mHealth and uHealth
BACKGROUND: Keystroke dynamics on smartphones have emerged as a promising form of passive digital biomarker. While previous studies have explored their utility in several diseases and disorders, relatively few have examined how these dynamics change ...

Uncovering age-specific subtypes of pediatric obesity and metabolic syndrome using machine learning algorithms.

Scientific reports
Identifying new subgroups among children and adolescents with obesity and metabolic syndrome requires advanced clustering techniques capable of analyzing complex multidimensional data. This study aimed to employ machine learning methods to enhance th...

Towards scalable age-grading of Aedes albopictus mosquito using mid-infrared spectroscopy and machine learning.

Scientific reports
The age structure and dynamics of mosquito populations are crucial for understanding their ability to spread diseases and assessing the effectiveness of anti-mosquito control measures. However, available methods to age-grade mosquito populations are ...

Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models.

Scientific reports
Acute Myeloid Leukemia (AML) is a genetically and clinically heterogeneous disease that can develop at any age. While AML incidence increases with age and distinct genetic alterations are observed in younger versus older patients, current classificat...