A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text
arXiv:2603.07487v1 Announce Type: new Abstract: Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end-to-end system to jointly optimize three-stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in-domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach
arXiv:2603.07487v1 Announce Type: new Abstract: Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end-to-end system to jointly optimize three-stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in-domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach could serve as a strong joint baseline for future research. The code is publicly available.
Executive Summary
This article introduces a novel end-to-end neural system that jointly models concept recognition, assertion classification, and relation extraction in clinical text—a previously underexplored joint task configuration. By shifting from independent task settings to a unified framework, the authors enable direct comparability with pipeline-based approaches. Empirical results demonstrate significant performance gains: +0.3 in concept F1, +1.4 in assertion F1, and +3.1 in relation F1 compared to the pipeline baseline across multiple embedding strategies. The work establishes a strong, publicly available baseline for future research in clinical information extraction. The study effectively bridges a critical gap between joint modeling and clinical NLP.
Key Points
- ▸ Joint task modeling introduced as a novel framework
- ▸ Substantial F1 improvements over pipeline baselines
- ▸ Public availability of code enhances reproducibility
Merits
Innovation
First to propose a joint end-to-end system for all three clinical extraction stages, enabling apples-to-apples comparison with existing pipelines.
Empirical Validation
Comprehensive evaluation across embedding types confirms tangible gains, lending credibility to the joint approach.
Demerits
Generalizability Concern
Performance gains may be domain-specific; applicability to non-clinical or broader text corpora remains unverified.
Evaluation Scope
Limited to existing embedding techniques; broader architectural innovations (e.g., transformer variants) are not explored.
Expert Commentary
The authors have made a pivotal contribution by reimagining the clinical information extraction pipeline as a single unified neural architecture. Historically, information extraction tasks were treated as sequential, discrete steps—this paper challenges that orthodoxy with compelling empirical evidence. The +3.1 F1 relative gain in relation extraction is particularly noteworthy, as relation extraction has traditionally lagged behind concept and assertion tasks. Moreover, the use of in-domain contextual embeddings as a differentiator suggests a nuanced understanding of domain-specific linguistic features. While the results are impressive, the study’s limitation lies in its lack of architectural diversity; future work should explore whether transformer-based variants or hybrid models can further enhance joint performance. Nevertheless, this work represents a paradigm shift in clinical NLP—it doesn’t just improve metrics; it redefines the conceptual architecture of the field.
Recommendations
- ✓ Future authors should benchmark their models against the proposed joint baseline as a standard.
- ✓ Investigate architectural variants—e.g., fusion of encoder-decoder or attention mechanisms—to further refine joint modeling.