Systematic identification of candidates for genetic testing using clinical data and machine learning

A method of training a machine-learning model to identify candidates for genetic testing, the method comprising:

• accessing a plurality of electronic health records, each electronic health record including a plurality of ICD codes;
• generating a set of phecodes for each electronic health record of the plurality of health records, the set of phecode being based at least in part on the plurality of ICD codes;
• determining, based on the electronic health record, a patient corresponding to the electronic health record has undergone a genetic test;
• and
• training the machine-learning model with a training set including, for each electronic health record, the generated set of phecodes and an indication of whether the patient has undergone the genetic test, wherein the machine-learning model is trained to receive as input a set of phecodes and to produce as output an indication of whether the patient corresponding to the set of phecodes is a candidate for the genetic test.

A system for evaluating electronic health record data to identify genetic disorders, the system comprising an electronic controller configured to:

• access, from a non-transitory computer-readable memory, electronic health record data for a patient;
• generate an input data set based on the electronic health record data, wherein the input data set is indicative of one or more phenotypes indicated by the electronic health record data;
• apply a trained artificial intelligence model to the input data set, wherein the trained artificial intelligence model is trained to produce an output indicating whether the patient is a candidate for genetic testing based on the one or more phenotypes indicated by the electronic health record data;
• and
• transmit an output signal in response to determining, based on the output of the trained artificial intelligence model, that the patient is a candidate for the genetic testing.