The Road to Digitally-Driven Mental Health Services

2022 ◽  
pp. 42-71
Author(s):  
Artemisa Rocha Dores ◽  
Andreia Geraldo ◽  
Helena Martins
Keyword(s):  
Mental Health ◽  
Data Mining ◽  
Technological Development ◽  
Data Mining Technique ◽  
Web Based ◽  
Positive Approach ◽  
Primordial Prevention ◽  
The Road ◽  
Computer Based ◽  
Theoretical Foundations

Intervention in mental health urges new solutions that merge solid theoretical foundations and new possibilities provided by technological development. This chapter is structured around results from a data mining technique using VOSViewer, which organized the field into five clusters of published literature: (1) most affected populations, (2) mental illness/disorders and their impact, (3) the expansion of remote interventions, (4) ICT potential to overcome limitations and (5) a positive approach to ICTs in mental health care. Solutions and recommendations are presented to overcome the issues identified, including how future interventions should consider old and new issues as the ones raised by the COVID-19 pandemic. Computer-based or web-based interventions are hereby presented as part of the revolution towards digital mental health or e-mental health. This approach has the potential to deconfine interventions, releasing them from the traditional settings and reaching new populations. It also reinforces the path already started, from the secondary to the primary and primordial prevention, towards the modification of the psychopathological trajectories.

scholarly journals Research on Personalized and Secured E-Learning Systems using Data Mining Technique

2019 ◽  
Vol 8 (11S) ◽  
pp. 664-667
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Learning Systems ◽  
Large Set ◽  
Data Mining Technique ◽  
Large Space ◽  
Web Based ◽  
Web Based Learning ◽  
E Learning ◽  
The Web

Data mining is the concept for extracting the appropriate data from the large set of database. In today’s world it is widely used for many applications where learning applications is one of the major part. The e-Learning is the booming technology where anyone can learn everything from any part of the world. It is the digital way of learning the concepts and does not require the help of other persons to do so. It also requires the large space for data storage such as user information, course records and course details and so on. There are lot of learning applications available on the internet among which some might be subjected to frauds. So the security is the demanding thing every users looking for to protect their details. The users also seek for flexibility of using the applications. In perspective of distributed world, the complexity and interoperability of the data brings challenges in e-learning domain.Depends upon learner’s choice, the web based learning modules were developed for the students. Thus, a holistic approach is required for achieving the personalized content since the student groups are heterogeneous in nature. In addition to, the personalized content has to be protected in order to maintain the data integrity and privacy of the users. In this work, we survey about the present scenario of the web-based e-learning systems. Initially, we present the services oriented architecture of the e-learning systems and also clearly explain the different elearning layers.Then, we portray the existing studies processed in web based e-learning systems. Finally, we discuss about the challenges still persists in web-based learning systems. This paper will guide the upcoming researchers in e-learning fields.

scholarly journals Analyzing the Accuracy of Answer Sheet Data in Paper-based Test Using Decision Tree

2019 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Edy Suharto ◽  
Aris Puji Widodo ◽  
Suryono Suryono
Keyword(s):  
Decision Tree ◽  
Evaluation Method ◽  
Single Layer ◽  
Answer Sheet ◽  
Data Mining Technique ◽  
Web Based ◽  
Tree Models ◽  
Computer Based ◽  
Using Data ◽  
Education Quality Assurance

In education quality assurance, the accuracy of test data is crucial. However, there is still a problem regarding to the possibility of incorrect data filled by test taker during paper-based test. On the contrary, this problem does not appear in computer-based test. In this study, a method was proposed in order to analyze the accuracy of answer sheet filling out in paper-based test using data mining technique. A single layer of data comprehension was added within the method instead of raw data. The results of the study were a web-based program for data pre-processing and decision tree models. There were 374 instances which were analyzed. The accuracy of answer sheet filling out attained 95.19% while the accuracy of classification varied from 99.47% to 100% depend on evaluation method chosen. This study could motivate the administrators for test improvement since it preferred computer-based test to paper-based.

ANALYSIS OF MESENCHYMAL STEM CELL DIFFERENTIATION IN VITRO USING CLASSIFICATION ASSOCIATION RULE MINING

2009 ◽  
Vol 07 (06) ◽  
pp. 905-930 ◽  
Author(s):  
WEIQI WANG ◽  
YANBO JUSTIN WANG ◽  
RENÉ BAÑARES-ALCÁNTARA ◽  
FRANS COENEN ◽  
ZHANFENG CUI
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Stem Cell Differentiation ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Web Based ◽  
Mesenchymal Stem Cell Differentiation ◽  
Classification Prediction

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

scholarly journals Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems

2015 ◽  
pp. 120-142 ◽  
Author(s):  
Marcos Aurélio Domingues ◽  
Alípio Mário Jorge ◽  
Carlos Soares ◽  
Solange Oliveira Rezende
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Web Mining ◽  
Support Systems ◽  
Web Documents ◽  
Web Based ◽  
Use Of Data ◽  
Computer Based

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information system that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.

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