Academic Journal of Computing & Information Science, 2019, 2(1); doi: 10.25236/AJCIS.010020.
Xinlei Chena, Dongming Zhaob, Wei Zhongc and Jiufeng Yed
Suzhou GuangJi Hospital, Suzhou 215137, China
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Mental health undertakings in Suzhou have developed in an all-round and healthy way. The "Suzhou Mental Health Specialist Alliance" led by Suzhou Mental Health Center was formally established. The aim is to set up a technology sharing platform to realize mutual sharing of medical resources; implement quality homogeneous management to improve the efficiency of specialized medical services; establish a pairing support mechanism to improve the level of prevention and treatment of specialized diseases at the grass-roots level; strengthen scientific and technological cooperation to enhance the academic research capacity of specialized hospitals; smooth the way of mass medical treatment and reduce the difficulty of referral of specialized patients. Using multi-source heterogeneous fusion algorithm technology to solve the problem of "data gap" in each unit of the alliance. Re-construct the information sharing platform of the spiritual specialty alliance with the spiritual specialty alliance as the main body from the technical framework. Communicate the data of major mental health institutions in Suzhou, and consider the corresponding security and privacy precautions from the security aspect. Guided by the information sharing platform of the Mental Health Alliance, we can give full play to the advantages of mental health specialty, better implement graded diagnosis and treatment and meet the health needs of the masses, and promote the comprehensive and healthy development of mental health in Suzhou.
Data fusion, heterogeneous systems, spiritual alliance, information sharing
Xinlei Chen, Dongming Zhao, Wei Zhong and Jiufeng Ye, Research on Information Sharing Technology of Mental Health Alliance Based on Multi-source Heterogeneous Data Fusion Algorithms. Academic Journal of Computing & Information Science (2019) Vol. 2: 74-80. https://doi.org/10.25236/AJCIS.010020.
 Melie-Garcia L, Draganski B, Ashburner J, et al. Multiple Linear Regression: Bayesian Inference for Distributed and Big Data in the Medical Informatics Platform of the Human Brain Project [J]. BioRxiv, 2018: 242883.
 Ilyasova N, Kupriyanov A, Paringer R, et al. Particular use of BIG DATA in medical diagnostic tasks [J]. Pattern Recognition and Image Analysis, 2018, 28 (1): 114-121.
 Zhang W, Yang J, Su H, et al. Medical data fusion algorithm based on internet of things [J]. Personal and Ubiquitous Computing, 2018, 22 (5-6): 895-902.
 Cheng X, Zhang L, Zheng Y. Deep similarity learning for multimodal medical images [J]. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2018, 6 (3): 248-252.
 El-Latif A A A, Abd-El-Atty B, Hossain M S, et al. Efficient quantum information hiding for remote medical image sharing [J]. IEEE Access, 2018, 6: 21075-21083.
 Arumugham S, Rajagopalan S, Rayappan J B B, et al. Networked medical data sharing on secure medium–A web publishing mode for DICOM viewer with three layer authentication [J]. Journal of biomedical informatics, 2018, 86: 90-105.
 Worden J W. Grief counseling and grief therapy: A handbook for the mental health practitioner [M]. Springer Publishing Company, 2018.