Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy.
Jörn LötschReetta SipiläTiina TasmuthDario KringelAnn-Mari EstlanderTuomo MeretojaEija KalsoAlfred UltschPublished in: Breast cancer research and treatment (2018)
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.
Keyphrases
- chronic pain
- pain management
- neuropathic pain
- machine learning
- end stage renal disease
- newly diagnosed
- ejection fraction
- minimally invasive
- chronic kidney disease
- prognostic factors
- high resolution
- peritoneal dialysis
- artificial intelligence
- atrial fibrillation
- patient reported outcomes
- postoperative pain
- acute coronary syndrome
- percutaneous coronary intervention
- breast cancer risk