ArticleFrontiers in pharmacology2026
AI-WAR: a novel warfarin management software with a bidirectional LSTM dosing model improves time in therapeutic range.
Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Warfarin remains the preferred anticoagulant in patients after heart valve replacement. However, its narrow therapeutic window, substantial interindividual variability, and dependence on frequent INR monitoring make effective management challenging. The quality of anticoagulation in real-world practice is often suboptimal, particularly in primary care settings with limited resources. Methods: We developed an artificial intelligence-based warfarin management system (AI-WAR) integrating remote follow-up, systematic data management, and individualized dosing prediction models. A randomized controlled trial (n = 624) and a prospective registry study (n = 176) were used for model training, while an independent real-world cohort (n = 200) served for external validation. We compared anticoagulation quality and clinical outcomes between conventional management and AI-WAR, and evaluated prediction performance of long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) models. Results: Compared with conventional management, AI-WAR significantly improved median TTR (48.7% vs. 81.3%, P < 0.001), increased time in target INR (36.3% vs. 61.9%, P < 0.001), and reduced both elevated INR percentage (12.0% vs. 16.8%) and overall adverse event rate (9.6% vs. 19.7%, P < 0.001). The Bi-LSTM model exhibited superior dose prediction accuracy (80.3% vs. 66.6%) and achieved 93.2% accuracy for stable dose prediction, while significantly reducing overdose predictions (11.9%, P < 0.001). Subgroup analyses demonstrated robust performance across different genotypes and in scenarios with missing genetic information. Conclusion: The integration of AI-WAR software and Bi-LSTM prediction models provides an effective and safe decision-support tool for warfarin individualized dosing. This system not only improves anticoagulation quality and reduces adverse events but also shows practical advantages in remote management and primary healthcare settings, supporting broader implementation of precision anticoagulation therapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.