A Web-Based Tool for Automatic Data Collection, Curation, and Visualization of Complex Healthcare Survey Studies including Social Network Analysis
<|reference_start|>A Web-Based Tool for Automatic Data Collection, Curation, and Visualization of Complex Healthcare Survey Studies including Social Network Analysis: There is a great concern nowadays regarding alcohol consumption and drug abuse, especially in young people. Analyzing the social environment where these adolescents are immersed, as well as a series of measures determining the alcohol abuse risk or personal situation and perception using a number of questionnaires like AUDIT, FAS, KIDSCREEN, and others, it is possible to gain insight into the current situation of a given individual regarding his/her consumption behavior. But this analysis, in order to be achieved, requires the use of tools that can ease the process of questionnaire creation, data gathering, curation and representation, and later analysis and visualization to the user. This research presents the design and construction of a web-based platform able to facilitate each of the mentioned processes by integrating the different phases into an intuitive system with a graphical user interface that hides the complexity underlying each of the questionnaires and techniques used and presenting the results in a flexible and visual way, avoiding any manual handling of data during the process. Advantages of this approach are shown and compared to the previous situation where some of the tasks were accomplished by time consuming and error prone manipulations of data.<|reference_end|>
@article{benítez-andrades2024a,
    title={A Web-Based Tool for Automatic Data Collection, Curation, and
  Visualization of Complex Healthcare Survey Studies including Social Network
  Analysis},
    author={Jos'e Alberto Ben'itez-Andrades, Jos'e Emilio Labra, Enedina
  Quiroga, Vicente Mart'in, Isa'ias Garc'ia, Pilar Marqu'es-S'anchez and
  Carmen Benavides},
    journal={Computation and Mathematical Methods in Medicine, Volume 2017,
  Article ID 2579848},
    year={2024},
    doi={10.1155/2017/2579848},
    archivePrefix={arXiv},
    eprint={2402.09592},
    primaryClass={cs.AI cs.HC}
}