An Ambient Agent Model for Support of Informal Caregivers during Stress

Aziz, Azizi Ab; Treur, Jan; Wal, C. Natalie van der

doi:10.1007/978-3-642-31087-4_52

Cited by 1 publication

(6 citation statements)

References 13 publications

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“…The studies in the reviewed articles were conducted from 2012 to 2023 across various countries, including the Netherlands [53], Thailand [55], Italy [47], Canada [56], Germany [50,52], Ireland [48,49], Korea [51], and United States [57].…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Eight of these studies primarily employed machine learning models [47][48][49][50][51][52]55,57], one utilized a chatbot [56], and another incorporated model-based reasoning [53]. Additionally, one study developed a novel health risk analysis system, based on a risk analysis classifier (RAC) and using the SCUT algorithm [55].…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Additionally, accuracy reflects the model's overall correctness, with values closer to 100% indicating higher reliability. Some studies have explored the development of an ambient agent to aid caregivers of depression patients, utilizing modelbased reasoning [53]. Meanwhile, other research has introduced advanced systems for evaluating caregivers' health risks across mental, physical, and social domains, offering tailored interventions.…”

Section: Implications and Key Findingsmentioning

confidence: 99%

“…Notably, their use of NNs yielded an accuracy rate exceeding 90% across all datasets. In another study by Aziz et al [53], based on [59], an ambient support for caregivers of patients with depression was developed and evaluated. They paid attention to three important aspects of caregiving, these being incoming stressors, mediating conditions, and caregiver outcomes.…”

Section: Mental Health Support For Family and Informal Caregiversmentioning

confidence: 99%

“…AI assistance may include robotic aid [51] and initiatives aimed at reducing the caregiving burden by improving caregivers' work-life balance [52]; however, challenges such as ethical concerns over invasions of privacy due to data recording, the risk of system malfunctions, or providing inaccurate information, may arise. Furthermore, caregivers might become excessively dependent on these AI services [53].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review

Borna,

Maniaci,

Haider

et al. 2024

Bioengineering

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This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being.

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