Data Integration of Electronic Health Records using Artificial Neural Networks

被引:0
|
作者
Gul, Omniyah [1 ]
Al-Qutayri, Mahmoud [1 ]
Quang Hieu Vu [2 ]
Yeun, Chan Yeob [1 ]
机构
[1] Khalifa Univ Sci Technol & Res, Abu Dhabi, U Arab Emirates
[2] Etisalat BT Innovat Ctr EBTIC, Abu Dhabi, U Arab Emirates
来源
2012 INTERNATIONAL CONFERENCE FOR INTERNET TECHNOLOGY AND SECURED TRANSACTIONS | 2012年
关键词
SYSTEM;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper presents a system that uses cloud computing to deliver a cost effective clinical care to patients. The central component of the proposal is an electronic health record system that acts as a patient centric model, enabling health care providers to access complete patient records from any location anytime. A hybrid cloud computing model is proposed to connect all health care facilities together at a national scale. However, due to the heterogeneity of clinical database schemas, a novel technique is needed for solving the problem of data heterogeneity such that all health care providers are capable of sharing electronic health records easily. In this paper, a semi-automated classification technique using Artificial Neural Networks is adopted to solve the problem of data integration. To prove the potential of the model, an experiment was carried out and promising results were achieved.
引用
收藏
页码:313 / 317
页数:5
相关论文
共 50 条
  • [11] A Novel Deep Similarity Learning Approach to Electronic Health Records Data
    Gupta, Vagisha
    Sachdeva, Shelly
    Bhalla, Subhash
    IEEE ACCESS, 2020, 8 : 209278 - 209295
  • [12] Improving immunization data management: an editorial on the potential of electronic health records
    Abramson, Erika
    Kaushal, Rainu
    Vest, Joshua
    EXPERT REVIEW OF VACCINES, 2014, 13 (02) : 189 - 191
  • [13] Research data warehouse: using electronic health records to conduct population-based observational studies
    Chen, Wansu
    Xie, Fagen
    Mccarthy, Don P.
    Reynolds, Kristi L.
    Lee, Mingsum
    Coleman, Karen J.
    Getahun, Darios
    Koebnick, Corinna
    Jacobsen, Steve J.
    JAMIA OPEN, 2023, 6 (02)
  • [14] Prediction of Diabetic Retinopathy Using Longitudinal Electronic Health Records
    Chen, Suhao
    Wang, Zekai
    Yao, Bing
    Liu, Tieming
    2022 IEEE 18TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2022, : 949 - 954
  • [15] Wetland Restoration Prioritization Using Artificial Neural Networks
    Maleki, Saeideh
    Soffianian, Ali Reza
    Koupaei, Saeid Soltani
    Baghdadi, Nicolas
    EL-Hajj, Mohamad
    Sheikholeslam, Farid
    Pourmanafi, Saeid
    WETLANDS, 2020, 40 (01) : 179 - 192
  • [16] The State of Population Health Surveillance Using Electronic Health Records: A Narrative Review
    Paul, Margaret M.
    Greene, Carolyn M.
    Newton-Dame, Remle
    Thorpe, Lorna E.
    Perlman, Sharon E.
    McVeigh, Katherine H.
    Gourevitch, Marc N.
    POPULATION HEALTH MANAGEMENT, 2015, 18 (03) : 209 - 216
  • [17] Associative attention networks for temporal relation extraction from electronic health records
    Zhao, Shiyi
    Li, Lishuang
    Lu, Hongbin
    Zhou, Anqiao
    Qian, Shuang
    JOURNAL OF BIOMEDICAL INFORMATICS, 2019, 99
  • [18] Prediction of groundwater drawdown using artificial neural networks
    Gholami, Vahid
    Sahour, Hossein
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (22) : 33544 - 33557
  • [19] Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions
    He, Zhenfang
    Zhang, Yaonan
    Guo, Qingchun
    Zhao, Xueru
    WATER RESOURCES MANAGEMENT, 2014, 28 (15) : 5297 - 5317
  • [20] Prediction of clothing comfort sensation of an undershirt using artificial neural networks with psychophysiological responses as input data
    Karasawa, Yuki
    Uemae, Mayumi
    Yoshida, Hiroaki
    Kamijo, Masayoshi
    TEXTILE RESEARCH JOURNAL, 2022, 92 (3-4) : 330 - 345