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Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy

Overview of attention for article published in Breast Cancer Research and Treatment, June 2018
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Title
Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy
Published in
Breast Cancer Research and Treatment, June 2018
DOI 10.1007/s10549-018-4841-8
Pubmed ID
Authors

Jörn Lötsch, Reetta Sipilä, Tiina Tasmuth, Dario Kringel, Ann-Mari Estlander, Tuomo Meretoja, Eija Kalso, Alfred Ultsch

Abstract

Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain. Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28-75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either "persisting pain" or "non-persisting pain" groups. Unsupervised machine learning was applied to map the parameters to these diagnoses. A symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with "yes/no" items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%. The present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 25%
Student > Bachelor 3 15%
Unspecified 3 15%
Professor 2 10%
Librarian 2 10%
Other 5 25%
Readers by discipline Count As %
Medicine and Dentistry 7 35%
Unspecified 6 30%
Engineering 2 10%
Computer Science 1 5%
Immunology and Microbiology 1 5%
Other 3 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 June 2018.
All research outputs
#11,599,745
of 13,055,200 outputs
Outputs from Breast Cancer Research and Treatment
#2,847
of 3,221 outputs
Outputs of similar age
#235,169
of 270,374 outputs
Outputs of similar age from Breast Cancer Research and Treatment
#56
of 62 outputs
Altmetric has tracked 13,055,200 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,221 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.