Data-Driven Smart Health in Asia Pacific

Permanent URI for this collection

Browse

Recent Submissions

Now showing 1 - 5 of 9
  • Item
    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.
  • Item
    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.
  • Item
    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.
  • Item
    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.
  • Item
    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.