Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial
| Autor: | Jan A. Hazelzet, Cornelis Verhoef, Linetta B. Koppert, Andrea L. Pusic, Laurentine S. E. van Egdom |
|---|---|
| Přispěvatelé: | Surgery, Public Health |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Short Communication Breast surgery medicine.medical_treatment education Breast Neoplasms Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Breast cancer SDG 3 - Good Health and Well-being Internal Medicine medicine Humans Patient Reported Outcome Measures Baseline (configuration management) Mastectomy business.industry medicine.disease humanities Surgery ComputingMethodologies_PATTERNRECOGNITION breast cancer surgery Oncology 030220 oncology & carcinogenesis Female Personalized medicine Artificial intelligence patient‐reported outcomes business computer Psychosocial Algorithms |
| Zdroj: | Breast Journal, 26(6), 1213-1215. Wiley-Blackwell Publishing Ltd The Breast Journal |
| ISSN: | 1524-4741 1075-122X |
| DOI: | 10.1111/tbj.13804 |
| Popis: | As high breast cancer survival rates are achieved nowadays, irrespective of type of surgery performed, prediction of long‐term physical, sexual, and psychosocial outcomes is very important in treatment decision‐making. Patient‐reported outcomes (PROs) can help facilitate this shared decision‐making. Given the significance of more personalized medicine and the growing trend on the application of machine learning techniques, we are striving to develop an algorithm using machine learning techniques to predict PROs in breast cancer patients treated with breast surgery. This short communication describes the bottlenecks in our attempt to predict PROs. |
| Databáze: | OpenAIRE |
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