6 papers
Learning from Lost Provenance: Multiple Instance Learning for Cancer Registry Tumor Group Classification
Leonard Ruocco, Jonathan Simkin, Lovedeep Gondara +2
Modernizing cancer registries with deep learning is opening new opportunities to automate labor-intensive tasks such as the coding of pathology reports. However, progress is constr…
ELM: A Hybrid Ensemble of Language Models for Automated Tumor Group Classification in Population-Based Cancer Registries
Lovedeep Gondara, Jonathan Simkin, Shebnum Devji +2
Background: Population-based cancer registries (PBCRs) manually extract data from unstructured pathology reports, a labor-intensive process where assigning reports to tumor groups…
Adapting Natural Language Processing Models Across Jurisdictions: A pilot Study in Canadian Cancer Registries
Jonathan Simkin, Lovedeep Gondara, Zeeshan Rizvi +5
Population-based cancer registries depend on pathology reports as their primary diagnostic source, yet manual abstraction is resource-intensive and contributes to delays in cancer…
Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare
Lovedeep Gondara, Jonathan Simkin, Graham Sayle +3
This study aims to guide language model selection by investigating: 1) the necessity of finetuning versus zero-shot usage, 2) the benefits of domain-adjacent versus generic pretrai…
Bridging AI Innovation and Healthcare Needs: Lessons Learned from Incorporating Modern NLP at The BC Cancer Registry
Lovedeep Gondara, Gregory Arbour, Raymond Ng +2
Automating data extraction from clinical documents offers significant potential to improve efficiency in healthcare settings, yet deploying Natural Language Processing (NLP) soluti…
A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting
Lovedeep Gondara, Jonathan Simkin
We present an audit mechanism for language models, with a focus on models deployed in the healthcare setting. Our proposed mechanism takes inspiration from clinical trial design wh…