Supervised machine learning tools: a tutorial for clinicians

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Medical Data Analysis

Background:

• The healthcare domain is increasingly leveraging big data for tangible benefits.
• Machine learning (ML) excels at identifying complex patterns in multi-dimensional data for predictions.
• Deep neural networks (DNNs) often outperform traditional ML methods in classification and regression tasks.

Purpose of the Study:

• To provide an accessible tutorial on supervised machine learning concepts and methods relevant to medicine.
• To demystify machine learning and illustrate its utility in medical applications.
• To offer practical suggestions for designing machine learning models for medical problems.

Main Methods:

• Overview of supervised machine learning concepts.
• Introduction to deep learning techniques.
• Discussion of practical model design considerations for medical applications.

Main Results:

• Demonstration of how machine learning models can be applied to healthcare.
• Explanation of deep learning's potential to surpass conventional ML methods.
• Guidance on structuring and implementing ML models for medical data.

Conclusions:

• Machine learning, including deep learning, offers significant potential for advancing healthcare.
• Accessible tutorials are crucial for broader adoption of AI in medicine.
• Proper model design is essential for successful implementation of AI in clinical settings.