Table 1.

Summary of ML Models Chosen as Most Optimal for the Clinical Task, and the Types of Data Used (ML models and data types sorted alphabetically).

Task	No. Studies	Chosen ML Models	Data Types Used
Disease Course	22	Bayes Network, Boosting, Decision Tree, Hierarchical Clustering, Neural Network, Partial Least Squares Discriminant Analysis, Random Forest, Regression, Support Vector Machine	Clinical, Gene Expression, Genetic, Imaging, Metabolomic, Metatranscriptomic, Microbiome
Diagnosis	18	Boosting, Hierarchical Clustering, Neural Network, Random Forest, Regression, Support Vector Machine	Gene Expression, Genetic, Imaging, Metabolomic, Microbiome
Disease Severity	16	Bayes Network, Boosting, Decision Tree, Hierarchical Clustering, Intelligent Monitoring, Neural Network, Regression, Support Vector Machine	Clinical, Gene Expression, Genetic, Imaging, Protein Biomarkers
Disease Subtype	8	Boosting, Hierarchical Clustering, Random Forest, Similarity Network Fusion Clustering, Support Vector Machine	Clinical, Gene Expression, Metabolomic, Microbiome
Treatment Response	7	Neural Network, Random Forest	Clinical, Gene Expression, Microbiome
Risk of Disease	6	Ensemble Model, Random Forest, Regression	Clinical, Gene Expression, Genetic
Patient Clustering	4	Gaussian Mixture Model, Hierarchical Clustering, Latent Dirichlet Allocation, Neural Network	Immunoassay, Metagenomic, Online Posts, Questionnaire
Medication Adherence	1	Support Vector Machine	Clinical
Metabolite Abundance	1	Sparse Neural Encoder-Decoder Network	Metabolomic, Microbiome
Identification of Patients	1	Natural Language Processing	Clinical