Uncovering complex relationships in biomedical data through hierarchical logic pattern contrast mining

Understanding the relationships which exist between features in medical data ranging from electronic health records to genetic variations in sequenced genomes is key to understanding how these features impact the medical condition of an individual. Existing pattern mining methods are unable to discover relationships more complex than co-occurrence which limits their usefulness in searching for patterns associated with medical conditions which may include inhibitory and many-to-one relationships between features. Our algorithm can extract complex nested logical relationships which can provide additional information about how individual features interact to affect medical outcomes. We demonstrate the effectiveness of our algorithm on two biomedical datasets, one with basic medical information from patients with and without glaucoma and the other containing SNP information from individuals diagnosed with autism and their non-autistic family members.