Literature DB >> 22231840

Data mining in psychiatric research.

Diego Tovar1, Eduardo Cornejo, Petros Xanthopoulos, Mario R Guarracino, Panos M Pardalos.   

Abstract

Mathematical sciences and computational methods have found new applications in fields like medicine over the last few decades. Modern data acquisition and data analysis protocols have been of great assistance to medical researchers and clinical scientists. Especially in psychiatry, technology and science have made new computational methods available to assist the development of predictive modeling and to identify diseases more accurately. Data mining (or knowledge discovery) aims to extract information from large datasets and solve challenging tasks, like patient assessment, early mental disease diagnosis, and drug efficacy assessment. Accurate and fast data analysis methods are very important, especially when dealing with severe psychiatric diseases like schizophrenia. In this paper, we focus on computational methods related to data analysis and more specifically to data mining. Then, we discuss some related research in the field of psychiatry.

Entities:  

Mesh:

Year:  2012        PMID: 22231840     DOI: 10.1007/978-1-61779-458-2_37

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  5 in total

Review 1.  Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.

Authors:  Susel Góngora Alonso; Isabel de la Torre-Díez; Sofiane Hamrioui; Miguel López-Coronado; Diego Calvo Barreno; Lola Morón Nozaleda; Manuel Franco
Journal:  J Med Syst       Date:  2018-07-21       Impact factor: 4.460

2.  A pilot study investigating changes in neural processing after mindfulness training in elite athletes.

Authors:  Lori Haase; April C May; Maryam Falahpour; Sara Isakovic; Alan N Simmons; Steven D Hickman; Thomas T Liu; Martin P Paulus
Journal:  Front Behav Neurosci       Date:  2015-08-27       Impact factor: 3.558

3.  Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study.

Authors:  Alina Haines-Delmont; Gurdit Chahal; Ashley Jane Bruen; Abbie Wall; Christina Tara Khan; Ramesh Sadashiv; David Fearnley
Journal:  JMIR Mhealth Uhealth       Date:  2020-06-26       Impact factor: 4.773

4.  Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.

Authors:  Susel Góngora Alonso; Gonçalo Marques; Deevyankar Agarwal; Isabel De la Torre Díez; Manuel Franco-Martín
Journal:  Sensors (Basel)       Date:  2022-03-25       Impact factor: 3.576

5.  Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

Authors:  Joanna F Dipnall; Julie A Pasco; Michael Berk; Lana J Williams; Seetal Dodd; Felice N Jacka; Denny Meyer
Journal:  PLoS One       Date:  2016-02-05       Impact factor: 3.240

  5 in total

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