2 posts tagged with "sapbert"

For twenty years, the question "which patients are most like this one?" has haunted clinical informatics. Molecular tumor boards want to know: of the 300 patients in our pancreatic cancer corpus, which ones had the same pathogenic variants, the same comorbidity profile, the same treatment history — and what happened to them? Population health researchers want to seed cohort definitions not from abstract inclusion criteria but from a concrete index patient. And every clinician who has ever stared at a complex case has wished for a button that says show me others like this.

Today, Parthenon ships that button. The Patient Similarity Engine is a multi-modal matching system that scores patients across six clinical dimensions — demographics, conditions, measurements, drugs, procedures, and genomic variants — with user-adjustable weights, dual algorithmic modes, bidirectional cohort integration, and tiered privacy controls. It works across any OMOP CDM source in the platform, from the 361-patient Pancreatic Cancer Corpus to the million-patient Acumenus CDM.

This post tells the story of why it was needed, what we studied before building it, how it works under the hood, and what we learned along the way.

Today we gave Parthenon's AI assistant a research library that most outcomes researchers would envy. Abby — our context-aware, privacy-preserving AI — now has 115,000+ SapBERT-embedded vectors spanning 2,258 peer-reviewed OHDSI papers, the complete Book of OHDSI, documentation from 30 HADES R packages, a decade of community forum Q&A, and 19 medical reference textbooks covering epidemiology, biostatistics, pharmacology, pathology, and clinical medicine.

This post tells the full story: why we built Abby, how the architecture works, what we harvested, what we learned about data quality in knowledge bases, and where we're headed next.