Classifier apparatus with decision support tool

A method of using at least one computing device to implement a clinical decision support tool for diagnosing a venous thromboembolism (VTE) condition for a patient, the method comprising:

• receiving, at the at least one computing device, genotypic and phenotypic physiological data;
• determining statistically significant relationships between the genotypic and phenotypic physiological data;
• determining an array of clusters of alleles as a spatial abstraction representing the statistically significant relationships, wherein determining the array comprises:
• determining, based at least in part on the genotype and phenotypic physiological data, which alleles are members of clusters and which alleles are not members of clusters;
• assigning the alleles that are cluster members, of a same cluster, to locations adjacent or in proximity to one another in the array;
• and
• assigning the alleles, that are not members of any cluster, randomly in the array;
• determining, at the at least one computing device, a classification model (a) based at least in part on training and using a deep learning convolutional neural network and (b) based at least in part on the array;
• and
• utilizing the classification model for a decision support application;
• wherein a treatment procedure is administered to the patient in association with the VTE condition based on determining the classification model and based further on utilizing the classification model for the decision support application.