TY  - JOUR
A1  - Lötsch, Jörn
A1  - Ultsch, Alfred
T1  - Machine learning in pain research
T2  - Pain
N2  - Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence. It has been accepted that pain is a complex phenomenon. Contemporary methods of computational science can use complex clinical and experimental data to better understand the complexity of pain. Among data science techniques, machine learning is referred to as a set of methods that can automatically detect patterns in data and then use the uncovered patterns to predict or classify future data, to observe structures such as subgroups in the data, or to extract information from the data suitable to derive new knowledge. Together with (bio)statistics, artificial intelligence and machine learning aim at learning from data. ...
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/50079
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-500799
SN  - 1872-6623
SN  - 0304-3959
N1  - Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
VL  - 159
IS  - 4
SP  - 623
EP  - 630
PB  - Lippincott Williams and Wilkins
CY  - New York, NY [u. a.]
ER  -