Exploring a Fuzzy Rule Inferred ConvLSTM for Discovering and Adjusting the Optimal Posture of Patients with a Smart Medical Bed.

Several countries nowadays are facing a tough social challenge caused by the aging population. This public health issue continues to impose strain on clinical healthcare, such as the need to prevent terminal patients' pressure ulcers. Provocative approaches to resolve this issue include health information technology (HIT). In this regard, this paper explores one technological solution based on a smart medical bed (SMB). By integrating a convolutional neural network (CNN) and long-short term memory (LSTM) model, we found this model enhanced performance compared to prior solutions. Further, we provide a fuzzy inferred solution that can control our proposed proprietary automated SMB layout to optimize patients' posture and mitigate pressure ulcers. Therefore, our proposed SMB can allow autonomous care to be given, helping prevent medical complications when lying down for a long time. Our proposed SMB also helps reduce the burden on primary caregivers in fighting against staff shortages due to public health issues such as the increasing aging population.

Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model.

It has been reported repeatedly that depression in middle-aged people may cause serious ramifications in public health. However, previous studies on this important research topic have focused on utilizing either traditional statistical methods (i.e., logistic regressions) or black-or-gray artificial intelligence (AI) methods (i.e., neural network, Support Vector Machine (SVM), ensemble). Previous studies lack suggesting more decision-maker-friendly methods, which need to produce clear interpretable results with information on cause and effect. For the sake of improving the quality of decisions of healthcare decision-makers, public health issues require identification of cause and effect information for any type of strategic healthcare initiative. In this sense, this paper proposes a novel approach to identify the main causes of depression in middle-aged people in Korea. The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network). The target dataset is a longitudinal dataset employed from the Korea National Health and Nutrition Examination Survey (KNHANES) database with a sample size of 8580. After developing the proposed Sons and Spouses Bayesian network model, we found thirteen main causes leading to depression. Then, genetic optimization was executed to reveal the most probable cause of depression in middle-aged people that would provide practical implications to field practitioners. Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.

The Unobserved Heterogeneneous Influence of Gamification and Novelty-Seeking Traits on Consumers' Repurchase Intention in the Omnichannel Retailing.

As information technology continues to provide a platform for any business willing to engage in diverse channels, it has ushered in a continuous evolution of ways to attract and maintain a given customer base. One of the latest trends seen in the retailing industry is the implementation of an omnichannel business strategy. As a result, the number of businesses now implementing such a strategy has led to a lack of differentiation amongst competitors. Therefore, it is no surprise that omnichannel retailers have had to rethink and acquire a new competitive advantage through the exploration of new and innovative strategic activities. Prior work on services has shown gamification to be a successful strategy in enhancing customer loyalty, promoting positive word-of-mouth, and enhancing greater engagement with the offered service. Also, offering hedonic values (one of gamification's main premises) has been an effective strategy for engaging customers as well as promoting repeat purchase intentions. Despite this, the potential effects of gamification within an omnichannel setting is not yet explored, and thus the rationale of this study. In exploring this gap, we employed means-end chain theory as a basis in which to discover the potential of gamification. Additionally, as gamification is a novel method in omnichannel research, this paper strived to explore the moderating effect of the novelty-seeking traits and unobserved heterogeneous behaviors of consumers. This research was based on 440 valid questionnaires in a survey dataset from Amazon M-Turk. The results provided strong evidence of the underlying proposition within the research models. Specifically, this study showed that gamification could be a potential unique feature used for engaging consumers onto one's platform, especially consumers with a novelty-seeking trait. We did, however, find that this finding does not apply to the case for all consumers. Therefore, the implication of this research suggests to practitioners that its implementation should be approached through an opt-in rather than a compulsory option.

Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis.

With the remarkable improvement in people's socioeconomic living standards around the world, adolescent obesity has increasingly become an important public health issue that cannot be ignored. Thus, we have implemented its use in an attempt to explore the viability of scenario-based simulations through the use of a data mining approach. In doing so, we wanted to explore the merits of using a General Bayesian Network (GBN) with What-If analysis while exploring how it can be utilized in other areas of public health. We analyzed data from the 2017 Korean Youth Health Behavior Survey conducted directly by the Korea Centers for Disease Control & Prevention, including 19 attributes and 11,206 individual data points. Our simulations found that by manipulating the amount of pocket money-between $60 and $80-coupled with a low-income background, it has a high potential to increase obesity compared with other simulated factors. Additionally, when we manipulated an increase in studying time with a mediocre academic performance, it was found to potentially increase pressure on adolescents, which subsequently led to an increased obesity outcome. Lastly, we found that when we manipulated an increase in a father's education level while manipulating a decrease in mother's education level, this had a large effect on the potential adolescent obesity level. Although obesity was the chosen case, this paper acts more as a proof of concept in analyzing public health through GBN and What-If analysis. Therefore, it aims to guide health professionals into potentially expanding their ability to simulate certain outcomes based on predicted changes in certain factors concerning future public health issues.