Data-Driven Smart Health in Asia Pacific

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    Intermittent Continuance of Smart Health Devices: A Zone-of-Tolerance Perspective
    ( 2020-01-07) Shen, Xiao-Liang ; Yang, Yimeng ; Sun, Yongqiang ; Li, Yang-Jun
    Smart health and wearable devices have recently received widespread attention from practitioners and scholars. However, intermittent continuance behavior of users is considered to be one of the most important reasons hindering the development of smart health. To address this issue, the current study employs the zone-of-tolerance theory to explore the mechanisms through which intermittent continuance is evoked. In particular, this study develops two new constructs (i.e., performance superiority and performance adequacy), and proposes that they affect intermittent continuance via satisfaction and neutral satisfaction, respectively. Results demonstrated that the effects of the two new variables on intermittent continuance of smart health devices had been fully mediated. This study concludes with theoretical and practical implications.
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    Design Of ASD Subtyping Approach based on Multi-Omics Data to Promote Personalized Healthcare
    ( 2020-01-07) Chen, Tao ; Lu, Peixin ; Lu, Long
    Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that has been confirmed to be related to some genetics risk factors which can lead to different clinical phenotypes. At present, ASD is mainly diagnosed based on some behavior and cognitive scales, which can not reveal the mechanism of disease occurrence, development and prognosis. In recent years, some studies have applied omics techniques into ASD research, but these studies are only based on single omics data source such as genomics, proteomics or transcriptomic without investigating ASD subtypes from integration of multi-omics data. In this study, we proposed an ASD subtyping framework that integrates clinical and multi-omics data to identify and analyze ASD subtypes at the molecular level. Due to the heterogeneity of different data modalities, a fusion clustering strategy was used to produce more accurate and interpretable clusters. Based on ASD subtyping results, we also proposed a classification framework to predict the subtype of new ASD patients. Deep learning method was used to extract features from each data modality, then all extracted features were integrated by the multiple kernel learning method to improve the classification accuracy.
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    Cost Optimization Estimation of Medical Institutions in Grading Diagnosis and Treatment based on System Dynamics Model
    ( 2020-01-07) Wu, Jiang ; Yao, Yao ; Huang, Xiao
    The grading diagnosis and treatment system in China is to improve the first-time diagnosis rate of patients in primary health care institutions, thereby increasing the proportion of people in primary health care institutions, thus achieving the goal of reducing costs. This study constructs a system dynamics model of the number of primary and upper-level visits—the cost of medical institutions, and simulates the effect of the increase in the proportion of of patients attending the primary level on the cost of medical institutions. The study found that with the increase in the number of visits, the cost of primary medical institutions will increase, but the total cost of the entire medical system will be reduced significantly. Moreover, the higher the proportion of the number of people attending the primary level, the lower the total cost. If the proportion of primary care in 2017 increases by 15%, and this trend is maintained until 2021, the total cost saved by medical institutions in 2021 will be as high as 903.32 billion yuan.
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    Research and Design of Autism Smart Diagnosis Information System Based on Chinese Children's Facial Expression Data and Deep Convolution Neural Network
    ( 2020-01-07) Zhao, Wang
    In this paper, the standard facial expression database FER2013 and CK + are used as the main training samples for autism diagnosis model.The facial expression image data of 16 Chinese children were collected as supplementary training samples.We use deep convolution neural network VGG19 and Resnet18 artificial intelligence algorithms to research and develop an smart information system for the diagnosis of autism through facial expression data.Ten normal children and ten autistic children were recruited for the comparative test to verify the accuracy of the system.After testing, the accuracy of facial expression recognition of this system reaches 81.4%.This research is based on the actual business needs of the hospital. The system can diagnose autism as early as possible,and promote the early treatment and rehabilitation of patients, thereby reducing the economic and mental burden of patients. Therefore, this smart information system has good social benefits and application value.
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    An investigation of Social Support Exchange and Communication Patterns among Chinese on Online Discussion Forum
    ( 2020-01-07) Wang, Xi ; Tong, Xing ; Zhu, Yushan ; Tan, Tianyi ; Zheng, Bowen
    Online Health Communities (OHCs), frequently adopted as online discussion forums for online users to communicate on health issues, have been used worldwide. By analyzing a representative breast-cancer-related OHC from mainland China—Baidu Discussion Forum, this study attempts to investigate social support exchange and communication patterns through user-generated content by data mining approaches. According to the outcomes, emotional support seeking and providing presents itself to be a more critical theme among Chinese users than other types of social support. In addition, almost half of the users on Baidu Discussion Forum have simple patterns of involvement, and a fairly small proportion of highly active Chinese users are quite influential in shaping the connections of the social support network. Meanwhile, the off-topic discussions which are not directly on health concerns are not frequently touched by Chinese people. This may impact the longevity of both users and threads, and undermine the foundation of OHCs in the long term. The findings have practical implications for researchers and health practitioners targeting on the Chinese population.
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    Research on the Evolution of Health Information Behavior From a Chinese Perspective
    ( 2020-01-07) Ding, Nian ; Huang, Xiao
    China has been undergoing a tremendous development in the reform of health system. It has great effects all the citizens and the nation as a whole. This paper aims to focus on the individuals from the aspect of information behavior. It is expected that the review on health information behavior could be conducted in a systematic way. Moreover, some statistical methods and software have been occupied in order to find out the entire progress of health information behavior. Specifically, both vertical and horizontal comparison have been conducted in this study, and a scientometric method has also be used. After a systematic and profound literature review, the whole progress has been explored and the main topics of great importance have been discovered. Moreover, highly cited papers and their relationship have also been revealed.
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    Examining the Role of Technology Anxiety and Health Anxiety on Elderly Users’ Continuance Intention for Mobile Health Services Use
    ( 2020-01-07) Meng, Fanbo ; Guo, Xitong ; Zhang, Xiaofei ; Peng, Zeyu ; Lai, Kee-Hung
    Mobile health (mHealth) is considered to be an important means of releasing the aging population problem. The efficiency of mHealth service can be increased by incorporating more elderly users and guaranteeing their continued use. However, limited attention has been directed toward investigating elderly users’ continuance intention for mHealth service use. Drawing upon the trust theory, we investigated elderly users’ characteristics, i.e. health anxiety and technology anxiety, to explain continuance intention. Survey data were collected comprising 261 valid responses to validate the research model and hypotheses. The results revealed that both cognitive and affective trust enhance continuance intention of mHealth services use. Health anxiety strengthens the effect of cognitive trust, but weakens the effect of affective trust, on the continuance intention. Furthermore, technology anxiety strengthens the effect of affective trust, but not that of cognitive trust, on the continuance intention. The limitations of our study and the theoretical and practical implications are discussed.
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    Service Failure and Consumers’ Satisfaction with the Healthcare Industry: Moderating Role of Recommendation
    ( 2020-01-07) Wang, Hao ; Liu, Shan ; Gao, Baojun ; Zhang, Jinlong
    This study explores the effects of service failure on different service attributes related to patients’ satisfaction (i.e., therapeutic effect and service attitude). We consider patients’ recommendation-seeking behavior and examine the moderating effects of recommendation before medical consultation and its differences between the online and offline word-of-mouth (WOM) recommendations. We collected over 3,000,000 reviews from a leading Chinese online health community to facilitate the empirical analysis. We use two ordinal logit models as bases and, find that service failure exerts a negative effect on patients’ both therapeutic effect satisfaction and service atti-tude satisfaction. Moreover, the effect of service fail-ure will be attenuated if patients seek recommenda-tions on doctors before consulting them. Moreover, the moderating effects of online WOM recommenda-tions is demonstrated to be lower than those of the offline ones. Our findings provide important perspectives for the literature and managerial suggestions for stakeholders.
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    Introduction to the Minitrack on Data-Driven Smart Health in Asia Pacific
    ( 2020-01-07) Ma, Feicheng ; Wu, Jiang ; Lu, Long