Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Table 2 Machine learning category, underlying principles, methods, and application examples

Machine learning category	Description	Machine learning techniques used	Algorithm	Uses	Ref.
Supervised learning	Supervised learning algorithm learns from labelled examples to train a model to predict future outcomes with high accuracy	Random forests, support vector machines, artificial neural networks	Classification, regression, sequence labelling	Predict treatment responses based on genomic profiles, aid in therapy selection	Nasteski et al[27]
Unsupervised learning	Unsupervised machine learning discerns patterns in unlabelled datasets to predict relationships and meaningful patterns	K-means clustering, principal component analysis	Clustering, dimensionality reduction	Identify patterns and relationships within patient data for treatment planning and prognostic analyses	Ghahramani[28]
Reinforcement learning	Reinforcement learning integrates user feedback to refine decision-making, enhancing the model's performance	Q-learning, Policy gradients	Sequential decision making	Optimize treatment selection by maximizing cumulative rewards over time	Sutton et al[29]