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:

• generating a respective first embedding vector for each of a plurality of terms associated with the rare disease, wherein the plurality of terms are obtained by using natural language processing on medical literature associated with a rare disease;
• receiving an initial dataset comprising respective medical data associated with a plurality of individuals with the 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;
• combining the initial dataset with a control dataset comprising respective medical data associated with a plurality of individuals without the rare disease to generate a combined dataset;
• and
• generating, for each individual in the combined dataset, a second embedding vector representing the individual based on features associated with the individual and the first embedding vectors for the plurality of terms associated with the rare disease, wherein the second embedding vectors form 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:

• generating a respective first embedding vector for each of a plurality of terms associated with the rare disease, wherein the plurality of terms are obtained by using natural language processing on medical literature associated with a rare disease;
• receiving an initial dataset comprising respective medical data associated with a plurality of individuals with the 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;
• combining the initial dataset with a control dataset comprising respective medical data associated with a plurality of individuals without the rare disease to generate a combined dataset;
• and
• generating, for each individual in the combined dataset, a second embedding vector representing the individual based on features associated with the individual and the first embedding vectors for the plurality of terms associated with the rare disease, wherein the second embedding vectors form a training dataset for training a machine learning model to identify individuals with the rare disease.