Causal framework for real-world evidence generation with language models

A computer-implemented method comprising:

• training a large language model (LLM), the LLM being configured to:
• receive, as at least one input, a medical document that includes medical text associated with a patient;
• and
• generate, as at least one output in response to the at least one input, one or more predicted values for one or more medical attributes of the patient based on the medical text included in the medical document;
• performing first attribute extraction from a plurality of structured medical documents of a plurality of patients, including extracting values for a first plurality of attributes associated with the plurality of patients;
• performing second attribute extraction from a plurality of unstructured medical documents of the plurality of patients using the LLM, including extracting predicted values for a second plurality of attributes associated with the plurality of patients;
• and
• performing a survival model simulation that computes estimations of hazard ratio (HR) between cases and controls using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction.

A computer storage device having computer-executable instructions stored thereon, which, on execution by a computer, cause the computer to perform operations comprising:

• training a large language model (LLM), the LLM being configured to:
• receive, as at least one input, a medical document that includes medical text associated with a patient;
• and
• generate, as at least one output in response to the at least one input, one or more predicted values for one or more medical attributes of the patient based on the medical text included in the medical document;
• performing first attribute extraction from a plurality of structured medical documents of a plurality of patients, including extracting values for a first plurality of attributes associated with the plurality of patients;
• performing second attribute extraction from a plurality of unstructured medical documents of the plurality of patients using the LLM, including extracting predicted values for a second plurality of attributes associated with the plurality of patients;
• and
• performing a survival model simulation that computes estimations of hazard ratio (HR) between cases and controls using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction.