Predicting patient-reported outcomes following surgery using machine learning Review
| Authors: | Hassan, A. M.; Biaggi-Ondina, A.; Rajesh, A.; Asaad, M.; Nelson, J. A.; Coert, J. H.; Mehrara, B. J.; Butler, C. E. |
| Review Title: | Predicting patient-reported outcomes following surgery using machine learning |
| Abstract: | Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field. © The Author(s) 2022. |
| Keywords: | patient satisfaction; postoperative period; outcome assessment; quality of life; breast reconstruction; algorithms; prediction; algorithm; artificial intelligence; surgery; clinical decision making; patient-reported outcomes; patient reported outcome measures; orthopedic surgery; spine surgery; artificial neural network; clinical outcome; patient-reported outcome; surgical oncology; machine learning; humans; human; article; random forest; least absolute shrinkage and selection operator; deep learning; minimal clinically important difference; k means clustering |
| Journal Title: | American Surgeon |
| Volume: | 89 |
| Issue: | 1 |
| ISSN: | 0003-1348 |
| Publisher: | Southeastern Surgical Congress |
| Date Published: | 2023-01-01 |
| Start Page: | 31 |
| End Page: | 35 |
| Language: | English |
| DOI: | 10.1177/00031348221109478 |
| PUBMED: | 35722685 |
| PROVIDER: | scopus |
| PMCID: | PMC9759616 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record. -- Export Date: 3 January 2023 -- Source: Scopus |
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