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An Introduction to Artificial Intelligence and Machine Learning in Addictions & Mental Health

Purpose

Key Questions: What are the types of machine learning?  How can machine learning be applied to addictions and mental health?

The concept of machines having human-like intelligence created the notion of artificial intelligence (AI) in the 1950s (Copeland, 2016). Since then, machine learning, a subfield of AI, has provided the most practical implementation of AI where computers can learn without being explicitly programmed (Copeland, 2016). This is done with sophisticated computer algorithms that extract information and patterns from data, and use this information to make predictions. Even though the idea of AI dates back to the 1950s, it is only within the last decade that machine learning has become such a powerful tool (Copeland, 2016).

This is because:

1. We now have access to large amounts of data from which the machine learning algorithms may ‘learn’.

2. Computational power and efficiency have improved significantly due to supercomputers and graphical processing units, allowing complicated algorithms to run quickly.

3. We can now address complicated problems by using artificial neural networks, a machine learning tool.

Contents

  • Types of Machine Learning
  • Artificial Neural Networks
  • Machine Learning in Addictions and Mental Health
    • Predictions from health records
    • Diagnostic imaging
    • Determining mental health via social media
    • Factors leading to addictions
Contact Person/Organization: 

Alberta Health Services 

Type of Tool:

Publication Date: 
Unknown
Videos: 

AI explained: How machine learning could save our healthcare system