This project presents a transparent, locally deployable post-NER de-identification framework that augments NER-based extraction with two refinement stages: a Verification loop for correcting candidate entities and a Candidate Expansion loop for recovering missed protected health information (PHI).
MedError, a machine-assisted, human-in-the-loop framework, improves the standardization, validity, and efficiency of clinical NLP error analysis, supporting more transparent and trustworthy model deployment.
Sun-Yang Fu, Qiu-Hao Lu, J. Ahn et al.· Nature Communications· 1 citation
Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two reviewers extract each study independently, an adjudicator resolves disagreements, and the team keeps an auditable record of how every value was produced. Large langua...
S. Kosuri, A. Bhosale, M. Glick et al.· 0 citations
Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have no evidence-based guidelines for follow-up testing, and structured encounter data do not capture the detail needed to support early detection and inform follow-up, including symptom duration, context, and fam-...
Nikkie Hooman, Monarch Nigam, Amy E. Hughes et al.· 0 citations
MedDeID, an on-premises framework combining in-house annotation and synthetic-note generation with model training, inference, pseudonymisation and evaluation, provides a route to locally governed de-identification using real or synthetic training data.
Stig Hellemans, T. Stroobants, E. Scheurwegs et al.· 0 citations
LLMs are suitable for information extraction of medications from clinical notes for use in research databases, however, for a clinical setting where the treatment of patients would be dependent on LLM performance, the current state-of-the-art open weight models are not accurate enough.
T. Spreuer, A. Günther, R. Majeed· Studies in Health Technology...· 0 citations
De-identifying temporal protected health information (PHI) is challenging because temporal expressions appear in diverse formats and must be transformed while preserving clinically meaningful timelines. This study proposes and evaluates an LLM-based framework for temporal PHI de-identification in real-world clinical no...
Xiao-Meng Wang, Li-Wei Wang, Andrew Wen et al.· medRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
