Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.

Area of Science:

• Orthopedic Surgery
• Biomedical Data Science
• Health Informatics

Background:

• Electronic health records (EHRs) generate large, high-dimensional datasets valuable for clinical care and research.
• Traditional statistical methods struggle with the complexity of high-dimensional data.
• Machine learning (ML) is emerging as a powerful tool for analyzing complex health data.

Purpose of the Study:

• To describe commonly used machine learning methods for structured data analysis.
• To provide examples of ML applications within orthopedic surgery.
• To offer practical guidance on initiating ML projects and evaluating ML research.

Main Methods:

• Review of common machine learning algorithms applicable to structured health data.
• Illustrative examples from orthopedic surgery research.
• Discussion of practical considerations for ML project implementation.

Main Results:

• Machine learning methods are effective in handling high-dimensional data from EHRs.
• ML facilitates the discovery of hidden patterns, classification, and prediction in patient data.
• The article provides a framework for applying and appraising ML in orthopedic research.

Conclusions:

• Machine learning offers significant advantages over conventional methods for analyzing complex EHR data.
• ML holds great potential for advancing patient care and research in orthopedic surgery.
• Guidance is provided for researchers entering the field of ML in healthcare.