A deep learning algorithm to prevent burnout risk in Family Caregivers of patients undergoing dialysis treatment

Costa, Vania; Messina, Martina; Bottone, Mario; Sperandeo, Raffaele; Esposito, Anna; Maldonato, Nelson Mauro; Cioffi, Valeria; Ronza, Giuseppina di; Iennaco, Daneiela; Dolce, Pasquale; Moretto, Enrico

doi:10.1109/coginfocom.2018.8639894

Cited by 1 publication

(7 citation statements)

References 26 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, one study developed a novel health risk analysis system, based on a risk analysis classifier (RAC) and using the SCUT algorithm [55]. Regarding validation methods, four studies [47,49,50,57] implemented Hold-Out validation, one solely used 10-fold CV [48], and another deployed both 2-fold CV and 10-fold CV [52]. It is noteworthy that two studies did not specify their validation methods [51,56].…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…In evaluating burnout risk among caregivers of dialysis patients and creating a stress measurement tool, one study reported a global accuracy of 78.70% and an AUC of 80.2% using their test sample for a deep neural network (DNN) model [47]. Using chatbots, one study examined providing customized, immediate emotional support to family caregivers at a non-profit organization [56].…”

Section: Implications and Key Findingsmentioning

confidence: 99%

“…These studies primarily investigate the application of AI to directly support informal caregivers, rather than indirectly through patient care. Methodologies vary from evaluating burnout risk among informal caregivers of patients undergoing dialysis or suffering from amyotrophic lateral sclerosis (ALS) to enhancing their quality of life [47][48][49], as well as providing caregiving knowledge [50]. 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.…”

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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