Creation and curation of knowledge graphs can accelerate disease discovery and analysis in real-world data. While disease ontologies aid in biological data annotation, codified categories (SNOMED-CT, ICD10, CPT) may not capture patient condition nuances or rare diseases. Multiple disease definitions across data sources complicate ontology mapping and disease clustering. We propose creating patient knowledge graphs using large language model extraction techniques, allowing data extraction via natural language rather than rigid ontological hierarchies. Our method maps to existing ontologies (MeSH, SNOMED-CT, RxNORM, HPO) to ground extracted entities. Using a large ambulatory care EHR database with 33.6M patients, we demonstrate our method through the patient search for Dravet syndrome, which received ICD10 recognition in October 2020. We describe our construction of patient-specific knowledge graphs and symptom-based patient searches. Using confirmed Dravet syndrome ICD10 codes as ground truth, we employ LLM-based entity extraction to characterize patients in grounded ontologies. We then apply this method to identify Beta-propeller protein-associated neurodegeneration (BPAN) patients, demonstrating real-world discovery where no ground truth exists.

A newly developed artificial intelligence (AI) system could help expedite the diagnosis of epileptic conditions such as Dravet syndrome. The AI system was described in a study, titled "A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome," in the journal Epilepsy & Behavior. Epilepsy is a broad disease category for many different conditions that involve seizures. Properly diagnosing epileptic conditions can be a challenge, especially given their different causes and symptoms. For example, mutations in the SCN1A gene are the most common cause of Dravet syndrome, but not all people with Dravet syndrome have such mutations, and SCN1A mutations can also be associated with other conditions, such as febrile seizures plus.