TY - JOUR
T1 - How data science can advance mental health research
AU - the MQ Data Science group
AU - Russ, Tom C.
AU - Woelbert, Eva
AU - Davis, Katrina A.S.
AU - Hafferty, Jonathan D.
AU - Ibrahim, Zina
AU - Inkster, Becky
AU - John, Ann
AU - Lee, William
AU - Maxwell, Margaret
AU - McIntosh, Andrew M.
AU - Stewart, Robert
AU - Anderson, Margaret
AU - Aylett, Kate
AU - Bourke, Suzy
AU - Burhouse, Anna
AU - Callard, Felicity
AU - Chapman, Kathy
AU - Cowley, Matt
AU - Cusack, James
AU - Delgadillo, Jaime
AU - Dix, Sophie
AU - Dobson, Richard
AU - Donohoe, Gary
AU - Dougall, Nadine
AU - Downs, Johnny
AU - Fisher, Helen
AU - Folarin, Amos
AU - Foley, Thomas
AU - Geddes, John
AU - Globerman, Joardana
AU - Hassan, Lamiece
AU - Hayes, Joseph
AU - Hodges, Helen
AU - Jacob, Eddie
AU - Jacobs, Rowena
AU - Joyce, Cynthia
AU - Kaur, Suky
AU - Kerz, Maximilian
AU - Kirkbride, James
AU - Leavey, Gerard
AU - Lewis, Glyn
AU - Lloyd, Keith
AU - Matcham, Wendy
AU - McCloskey, Erin
AU - McQuillin, Andrew
AU - Delgado, Tamsin Newlove
AU - Newsome, Catherine
AU - Nicodemus, Kristin
AU - Porteous, David
AU - Ray, Daniel
AU - Sanhu, Simran
AU - Smith, Daniel
AU - Tutu, Laura
AU - Ullah, Ayath
AU - Vance, Bill
AU - Wolpert, Miranda
AU - Wyse, Cathy
AU - Zammit, Stanley
N1 - Publisher Copyright:
© 2018, Springer Nature Limited.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.
AB - Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.
UR - https://www.scopus.com/pages/publications/85058409825
U2 - 10.1038/s41562-018-0470-9
DO - 10.1038/s41562-018-0470-9
M3 - Article
SN - 2397-3374
VL - 3
SP - 24
EP - 32
JO - Nature human behaviour
JF - Nature human behaviour
IS - 1
ER -