LEADER 04446nam 22005175 450 001 9910337509703321 005 20200706120104.0 010 $a3-030-03553-0 024 7 $a10.1007/978-3-030-03553-2 035 $a(CKB)4100000007656710 035 $a(DE-He213)978-3-030-03553-2 035 $a(MiAaPQ)EBC5702827 035 $a(PPN)235006734 035 $a(EXLCZ)994100000007656710 100 $a20190212d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPersonalized Psychiatry $eBig Data Analytics in Mental Health /$fedited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XV, 180 p. 24 illus., 21 illus. in color.) 311 $a3-030-03552-2 327 $a1. Big data and Machine Learning Techniques Meet Health Sciences -- 2. Major challenges and limitations of Big data analytics -- 3. A Clinical Perspective on Big Data in Mental Health -- 4. Big Data Guided Interventions: Predicting Treatment Response -- 5. The role of big data analytics in predicting suicide -- 6. Emerging Shifts in Neuroimaging Data Analysis in the Era of ?Big Data" -- 7. Phenomapping: methods and measures for deconstructing diagnosis in psychiatry -- 8. How to integrate data from multiple biological layers in mental health? -- 9. Ethics in the Era of Big Data. 330 $aThis book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Personalized Psychiatry ? Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health. . 606 $aPsychiatry 606 $aBig data 606 $aPsychiatry$3https://scigraph.springernature.com/ontologies/product-market-codes/H53003 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aPsychiatry. 615 0$aBig data. 615 14$aPsychiatry. 615 24$aBig Data/Analytics. 615 24$aBig Data. 676 $a616.89 676 $a616.8900285 702 $aPassos$b Ives Cavalcante$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMwangi$b Benson$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKapczinski$b Flávio$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910337509703321 996 $aPersonalized Psychiatry$91742960 997 $aUNINA