TY  - JOUR
A1  - Lötsch, Jörn
A1  - Sipilä, Reetta M.
A1  - Dimova, Violeta
A1  - Kalso, Eija
T1  - Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery
T2  - British journal of anaesthesia
N2  - Background: Prevention of persistent pain after breast cancer surgery, via early identification of patients at high risk, is a clinical need. Psychological factors are among the most consistently proposed predictive parameters for the development of persistent pain. However, repeated use of long psychological questionnaires in this context may be exhaustive for a patient and inconvenient in everyday clinical practice.
Methods: Supervised machine learning was used to create a short form of questionnaires that would provide the same predictive performance of pain persistence as the full questionnaires in a cohort of 1000 women followed up for 3 yr after breast cancer surgery. Machine-learned predictors were first trained with the full-item set of Beck's Depression Inventory (BDI), Spielberger's State–Trait Anxiety Inventory (STAI), and the State–Trait Anger Expression Inventory (STAXI-2). Subsequently, features were selected from the questionnaires to create predictors having a reduced set of items.
Results: A combined seven-item set of 10% of the original psychological questions from STAI and BDI, provided the same predictive performance parameters as the full questionnaires for the development of persistent postsurgical pain. The seven-item version offers a shorter and at least as accurate identification of women in whom pain persistence is unlikely (almost 95% negative predictive value).
Conclusions: Using a data-driven machine-learning approach, a short list of seven items from BDI and STAI is proposed as a basis for a predictive tool for the persistence of pain after breast cancer surgery.
KW  - persisting pain
KW  - psychological questionnaires
KW  - breast cancer
KW  - patients
KW  - machine-learning
KW  - data science
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/48865
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-488658
SN  - 1471-6771
SN  - 0007-0912
N1  - © 2018 The Author(s). Published by Elsevier Ltd on behalf of British Journal of Anaesthesia. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
VL  - 121
IS  - 5
SP  - 1123
EP  - 1132
PB  - Elsevier
CY  - [Amsterdam]
ER  -