Supervised machine learning and associated algorithms: applications in orthopedic surgery.

Area of Science:

• Medical research
• Machine learning applications in healthcare
• Orthopedic literature

Background:

• Supervised learning is widely used in medical research for outcome prediction and case classification.
• Increasing prevalence of complex machine learning models, like tree boosting, alongside "big data" initiatives.
• A gap exists in literature detailing the strengths and limitations of various supervised learning techniques.

Purpose of the Study:

• To provide an overview of commonly used supervised learning techniques in medical research.
• To present recent case examples specifically within the orthopedic literature.
• To address disparities in understanding these methods and enhance communication among research teams.

Main Methods:

• Review of supervised learning techniques, including traditional regression and advanced tree boosting.
• Analysis of recent case examples from orthopedic research applying these methods.
• Discussion of the strengths and limitations inherent to each technique.

Main Results:

• Identified common supervised learning techniques and their applications in orthopedics.
• Highlighted the need for better understanding of model strengths and limitations.
• Emphasized the importance of formal training for healthcare professionals in machine learning.

Conclusions:

• Improved comprehension of supervised learning methods is crucial for effective medical research.
• Bridging the knowledge gap in machine learning enhances collaboration and application in healthcare.
• This overview aims to foster better communication and utilization of machine learning in medicine.