• Journal of Preventive Medicine and Public Health
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• v.30 no.3 s.58
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• pp.630-642
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• 1997

The purpose of this study was to examine the mortality risk associated with cognitive impairment among the rural elderly. The subjective of study was 558 of 'A Study on the Depression and Cognitive Impairment in the Rural Elderly' of Jung Ae Rhee and Hyang Gyun Jung's study(1993). Cognitive impairment and other social and health factors were assessed in 558 elderly rural community residents. For this study, a Korean version of the Mini-Mental State Examination(MMSEK) was used as a global indicator of cognitive functioning. And mortality risk factors for each cognitive impairment subgroup were identified by univariate and multivariate Cox regression analysis. At baseline 22.6% of the sample were mildly impaired and 14.2% were severely impaired. As the age increased, the cognitive function was more impaired. Sexual difference was existed in the cognitive function level. Also the variables such as smoking habits, physical disorders had the significant relationship with cognitive function impairment. Across a 3-year observation period the mortality rate was 8.5% for the cognitively unimpaired, 11.1% for the mildly impaired, and 16.5% for the severly impaired respendents. And the survival probability was .92 for the cognitively unimpaired, .90 for the mildly impaired, and .86 for the severly impaired respondents. Compared to survival curve for the cognitively unimpaired group, each survival curve for the mildly and the severely impaired group was not significantly different. When adjustments models were not made for the effects of other health and social covariates, each hazard ratio of death of mildly and severely impaired persons was not significantly different as compared with the cognitively unimpaired. But, as MMSEK score increased, significantly hazard ratio of death decreased. Employing Cox univariate proportional hazards model, statistically other significant variables were age, monthly income, smoking habits, physical disorders. Also when adjustments were made for the effects of other health and social covariates, there was no difference in hazard ratio of death between those with severe or mild impairment and unimpaired persons. And as MMSEK score increased, significantly hazard ratio of death did not decrease. Employing Cox multivariate proportional hazards model, statistically other significant variables were age, monthly income, physical disorders. Employing Cox multivariate proportional hazards model by sex, at men and women statistically significant variable was only age. For both men and women, also cognitive impairment was not a significant risk factor. Other investigators have found that cognitive impairment is a significant predictor of mortality. But we didn't find that it is a significant predictor of mortality. Even though the conclusions of our study were not related to cognitive impairment and mortality, early detection of impaired cognition and attention to associated health problems could improve the quality of life of these older adults and perhaps extend their survival.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.