Identifying incident medication-related osteonecrosis of the jaw and antiresorptive drug holidays using natural language processing on clinical notes in men and women prescribed bisphosphonates or denosumab for fracture prevention

Objective Medication-related osteonecrosis of the jaw (MRONJ) is a rare complication of antiresorptive therapy for osteoporosis, with risk potentially influenced by "drug holidays" around dentoalveolar procedures. MRONJ risk is higher in rheumatic diseases. Epidemiologic studies are limited by poor administrative code performance for MRONJ identification and lack of structured pharmacy data capturing drug holidays. We developed and validated a natural language processing (NLP) algorithm to classify MRONJ and det…

By the Truvace deskOctober 4, 20261 min read1 sourcePeer-reviewedTRV-2026-1272· cite & verify

In brief

Investigators developed and validated a natural language processing algorithm to identify incident medication-related osteonecrosis of the jaw and antiresorptive drug holidays from free-text clinical notes, using Veterans Health Administration records for adults 50 and older prescribed bisphosphonates or denosumab for fracture prevention between 1999 and 2022.

Accurate identification matters because MRONJ is a rare but serious complication where administrative codes perform poorly and pharmacy data miss drug holidays, especially relevant for higher-risk rheumatic disease populations; uncertainty remains about performance outside the VA system and in more diverse, non-veteran cohorts.

Main points

Study used U.S. Veterans Health Administration EHR for adults >=50 with >=1 filled prescription for bisphosphonate or denosumab for fracture prevention from 10/1/1999-12/31/2022.
Reference set included 6,421 annotations informing a curated vocabulary of >1,350 terms, with 11 targets and 16 attributes defined in NLP codebook.
Decision tree-based machine-learning model used NLP-extracted features to classify MRONJ, while drug holiday was an NLP target directly.

The gain

NLP model classified incident MRONJ and identified antiresorptive drug holidays from free-text EHR notes with high precision and recall in validation sets.

The rundown

Researchers built an NLP codebook with 11 targets and 16 attributes, annotated 870 notes with two independent annotators plus 100 notes for independent validation, producing 6,421 annotations and a vocabulary of over 1,350 terms.

Performance was reported for MRONJ classification excluding unclassifiable outputs and for drug holiday detection in held-out validation, with authors concluding the algorithm is sufficiently accurate to be a promising tool for future studies including in persons with rheumatic diseases.