Predicting patient-reported outcomes following surgery using machine learning
Patient-reported outcomes (PROs) enable providers to identify differences in treatment
effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a …
effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a …
Machine learning and surgical outcomes prediction: a systematic review
Background Machine learning (ML) has garnered increasing attention as a means to
quantitatively analyze the growing and complex medical data to improve individualized …
quantitatively analyze the growing and complex medical data to improve individualized …
A surgeon's guide to artificial intelligence-driven predictive models
Artificial intelligence (AI) focuses on processing and interpreting complex information as well
as identifying relationships and patterns among complex data. Artificial intelligence-and …
as identifying relationships and patterns among complex data. Artificial intelligence-and …
[HTML][HTML] Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians
G Brydges, A Uppal, V Gottumukkala - Current Oncology, 2024 - mdpi.com
This narrative review explores the utilization of machine learning (ML) and artificial
intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer …
intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer …
Artificial intelligence and machine learning in prediction of surgical complications: current state, applications, and implications
Surgical complications pose significant challenges for surgeons, patients, and health care
systems as they may result in patient distress, suboptimal outcomes, and higher health care …
systems as they may result in patient distress, suboptimal outcomes, and higher health care …
[HTML][HTML] Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning
Background Machine-learning classifiers mostly offer good predictive performance and are
increasingly used to support shared decision-making in clinical practice. Focusing on …
increasingly used to support shared decision-making in clinical practice. Focusing on …
Patients like you: how machine learning can be used as a shared decision-making tool to improve care
AZ Virji, CW Brennan, L Skrabonja… - … Innovations in Care …, 2021 - catalyst.nejm.org
By collecting a practice's historic registry data and associated patient reported outcomes and
incorporating a machine learning model, clinicians can create personalized shared decision …
incorporating a machine learning model, clinicians can create personalized shared decision …
Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications
Importance Postoperative complications can significantly impact perioperative care
management and planning. Objectives To assess machine learning (ML) models for …
management and planning. Objectives To assess machine learning (ML) models for …
Path from predictive analytics to improved patient outcomes: a framework to guide use, implementation, and evaluation of accurate surgical predictive models
AHS Harris - Annals of surgery, 2017 - journals.lww.com
An entire industry is booming on the promise that electronic health predictive analytics (e-
HPA) can improve surgical outcomes by, for example, predicting whether a procedure is …
HPA) can improve surgical outcomes by, for example, predicting whether a procedure is …
Performance of a machine learning algorithm using electronic health record data to predict postoperative complications and report on a mobile platform
Importance Predicting postoperative complications has the potential to inform shared
decisions regarding the appropriateness of surgical procedures, targeted risk-reduction …
decisions regarding the appropriateness of surgical procedures, targeted risk-reduction …