Machine learning systems and methods to diagnose rare diseases

A system comprising one or more processors and a memory, the memory comprising computer readable code that, when executed by the one or more processors, causes the system to perform operations comprising:

• receiving an initial dataset comprising respective medical data associated with a plurality of individuals with a rare disease, the respective medical data for each individual comprising data indicative of a plurality of features of the rare disease experienced by the individual;
• identifying a plurality of clusters of individuals whose medical data is in the initial dataset by using an unsupervised clustering method;
• identifying, from among the clusters, one or more least-representative clusters as being least representative of the rare disease based on the medical data of the individuals in the clusters;
• removing from the initial dataset medical data of one or more individuals in the one or more least-representative clusters based on the medical data of the one or more individuals to generate a pruned dataset;
• and
• combining the pruned dataset with a control dataset comprising medical data of a plurality of individuals without the rare disease to generate a training dataset for training a machine learning model to identify individuals with the rare disease.

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• receiving an initial dataset comprising respective medical data associated with a plurality of individuals with a rare disease, the respective medical data for each individual comprising data indicative of a plurality of features of the rare disease experienced by the individual;
• identifying a plurality of clusters of individuals whose medical data is in the initial dataset by using an unsupervised clustering method;
• identifying, from among the clusters, one or more least-representative clusters as being least representative of the rare disease based on the medical data of the individuals in the clusters;
• removing from the initial dataset medical data of one or more individuals in the one or more least-representative clusters based on the medical data of the one or more individuals to generate a pruned dataset;
• and
• combining the pruned dataset with a control dataset comprising medical data of a plurality of individuals without the rare disease to generate a training dataset for training a machine learning model to identify individuals with the rare disease.