Social determinants of the association among cerebrovascular disease, hearing loss and cognitive impairment in a middle‐aged or older population: Recurrent neural network analysis of the Korean Longitudinal Study of Aging (2014–2016)

Lee, Kwang-Sig; Park, Kun Woo

doi:10.1111/ggi.13716

Cited by 20 publications

(24 citation statements)

References 15 publications

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“…This operation of "convolution" identifies certain characteristics of the input data, for example, the shape of a tumor compared to that of a normal cell [44]. In the recurrent neural network, the current output information depends, in a repetitive (or "recurrent") pattern, on the current input information and the previous hidden state (which is the memory of the network on what happened in all previous periods) [45]. Acquiring big data that is also high quality is essential for these cutting-edge approaches to be effective and, for this reason, there is little or no such endeavor in case big data is not available (as for preterm birth).…”

Section: Application Of Deep Learning In Early Diagnosis Of Spontaneomentioning

confidence: 99%

Application of Artificial Intelligence in Early Diagnosis of Spontaneous Preterm Labor and Birth

Lee

Ahn

2020

Diagnostics

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This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth (“preterm birth” hereafter). The summary of review suggests that different machine learning approaches would be optimal for different types of data regarding the prediction of preterm birth: the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohysterogram data; the recurrent neural network for text data; and the convolutional neural network for image data. The ranges of performance measures were 0.79–0.94 for accuracy, 0.22–0.97 for sensitivity, 0.86–1.00 for specificity, and 0.54–0.83 for the area under the receiver operating characteristic curve. The following maternal variables were reported to be major determinants of preterm birth: delivery and pregestational body mass index, age, parity, predelivery systolic and diastolic blood pressure, twins, below high school graduation, infant sex, prior preterm birth, progesterone medication history, upper gastrointestinal tract symptom, gastroesophageal reflux disease, Helicobacter pylori, urban region, calcium channel blocker medication history, gestational diabetes mellitus, prior cone biopsy, cervical length, myomas and adenomyosis, insurance, marriage, religion, systemic lupus erythematosus, hydroxychloroquine sulfate, and increased cerebrospinal fluid and reduced cortical folding due to impaired brain growth.

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Section: Application Of Deep Learning In Early Diagnosis Of Spontaneomentioning

confidence: 99%

Application of Artificial Intelligence in Early Diagnosis of Spontaneous Preterm Labor and Birth

Lee

Ahn

2020

Diagnostics

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“…In addition, DL models can also help identify putative preventive measures (i.e. family support, socioeconomic status and friendship activity) in managing cerebrovascular disease, hearing loss and cognitive impairment in middle-aged and older adults [25].…”

Section: Examples Of ML and Dl Implementation In The Medical Care Of mentioning

confidence: 99%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

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Clinical applications of Artifi cial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis infl uencing general healthcare have not materialized, with few exceptions, and then predominantly in the fi eld of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient's care, as well as streamline workfl ow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people. Key points: 1. Artifi cial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care. 2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations. 3. Telemedicine and robot assisted technology are well received by older service users. 4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting. seem to face a number of barriers in contacting communitybased primary health care, such as limited access, lack of standardized information systems and care pathways [3], all necessary to address their complex health care and social care needs. Indeed, older adults have much higher prevalence of nearly all major chronic and long-term conditions. In addition, they are more likely to succumb to adverse health events, such as a falls or infections, and these can lead to dramatic changes in their physical and mental wellbeing even after an apparently minor incident [4]. However, person and familyfocused care, self-management resources, and successful collaborative practice have been all highlighted as facilitators of good health care provision both by older people and their families [3]. All the above places the importance of diagnosis, monitoring of disease risks and their prevention, as well as management and optimizing of geriatric syndromes in the community for both older people living independenly or in 24h care facilities. In particular, identi ication of health issues/ diagnosis, support/treatment needs evaluation, development

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“…Six machine learning approaches were applied for the prediction of the disease association: RNN, logistic regression, decision tree, naïve Bayes, random forest and support vector machine. 7,[13][14][15][16][17][18][19] The disease association in Y2016 served as the dependent variable of the models. Cerebrovascular disease, hearing loss and cognitive impairment in Y2014 and the demographic, socioeconomic and health-related factors in Y2014 served as the independent variables of the models.…”

Section: Methodsmentioning

confidence: 99%

“…A previous study used an artificial-intelligence model (RNN, recurrent neural network) for evaluating 1) whether social determinants are major determinants of association among the 3 diseases in a middle-aged or old population (Hypothesis 1) and 2) whether association among the 3 diseases is very strong in the middle-aged or old (Hypothesis 2). 7 The source of data for this research was the Korean Longitudinal Study of Ageing (2014-2016), with 6,060 participants aged 53 years or more. The dependent variable of this research, association among the 3 diseases, had 8 categories, that is, 1 category for having no disease, 3 categories for having 1, 3 categories for having 2, and 1 category for having 3.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approaches to Social Determinants of Cognitive Impairment and Its Associated Conditions

Lee

Park

2020

Dement Neurocogn Disord

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Background and Purpose: This study uses an artificial-intelligence model (recurrent neural network) for evaluating the following hypothesis: social determinants of disease association in a middle-aged or old population are different across gender and age groups. Here, the disease association indicates an association among cerebrovascular disease, hearing loss and cognitive impairment. Methods: Data came from the Korean Longitudinal Study of Ageing (2014-2016), with 6,060 participants aged 53 years or more, that is, 2,556 men, 3,504 women, 3,640 aged 70 years or less (70−), 2,420 aged 71 years or more (71+). The disease association was divided into 8 categories: 1 category for having no disease, 3 categories for having 1, 3 categories for having 2, and 1 category for having 3. Variable importance, the effect of a variable on model performance, was used for finding important social determinants of the disease association in a particular gender/age group, and evaluating the hypothesis above. Results: Based on variable importance from the recurrent neural network, important social determinants of the disease association were different across gender and age groups: 1) leisure activity for men; 2) parents alive, income and economic activity for women; 3) children alive, education and family activity for 70−; and 4) brothers/sisters cohabiting, religious activity and leisure activity for 70+. Conclusions: The findings of this study support the hypothesis, suggesting the development of new guidelines reflecting different social determinants of the disease association across gender and age groups.

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Social determinants of the association among cerebrovascular disease, hearing loss and cognitive impairment in a middle‐aged or older population: Recurrent neural network analysis of the Korean Longitudinal Study of Aging (2014–2016)

Cited by 20 publications

References 15 publications

Application of Artificial Intelligence in Early Diagnosis of Spontaneous Preterm Labor and Birth

Application of Artificial Intelligence in Early Diagnosis of Spontaneous Preterm Labor and Birth

Artificial Intelligence in the healthcare of older people

Artificial Intelligence Approaches to Social Determinants of Cognitive Impairment and Its Associated Conditions

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