AIMC Topic: Middle Aged

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Understanding Daily Care Experience Preferences Across the Lifespan of Older Adults: Application of Natural Language Processing.

Western journal of nursing research
INTRODUCTION: Older adults are a heterogeneous group, and their care experience preferences are likely to be diverse and individualized. Thus, the aim of this study was to identify categories of older adults' care experience preferences and to examin...

Longitudinal changes following the introduction of socially assistive robots in nursing homes: a qualitative study with ICF framework and causal loop diagramming.

BMC geriatrics
BACKGROUND: Socially assistive robots introduced in nursing care settings have multidimensional psychological impacts on care recipients and caregivers. This study aims to explore the longitudinal changes induced by socially assistive robots, focusin...

Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms.

Journal of neurology
PURPOSE: Embolic stroke of unidentified source (ESUS) represents 10-25% of all ischemic strokes. Our goal was to determine whether ESUS could be reclassified to cardioembolic (CE) or large-artery atherosclerosis (LAA) with machine learning (ML) using...

Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study.

Cancer immunology, immunotherapy : CII
BACKGROUND: Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 stud...

Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals' perspectives.

BMC medical ethics
BACKGROUND: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are increasingly being integrated into healthcare for various purposes, including resource allocation. While these systems promise improved efficiency and decision...

Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This ...

First comparison between artificial intelligence-guided coronary computed tomography angiography versus single-photon emission computed tomography testing for ischemia in clinical practice.

Coronary artery disease
BACKGROUND: Noninvasive cardiac testing with coronary computed tomography angiography (CCTA) and single-photon emission computed tomography (SPECT) are becoming alternatives to invasive angiography for the evaluation of obstructive coronary artery di...

Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.

European radiology
OBJECTIVES: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial inte...

CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer.

European radiology
BACKGROUND: Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk...