@inproceedings{valiev-2026-hse,
title = "{HSE} {NLP} {TEAM} at {MEDIQA}-{SYNUR} 2026: Consensus Adjudication Ensemble ({ACE}): Balancing Precision and Recall for Schema-Bystander Clinical Extraction",
author = "Valiev, Airat A.",
editor = "Ben Abacha, Asma and
Bethard, Steven and
Bitterman, Danielle and
Naumann, Tristan and
Roberts, Kirk",
booktitle = "Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical {NLP}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.clinicalnlp-1.22/",
doi = "10.63317/4vsjfsrfx246",
pages = "200--211",
abstract = "Clinical documentation from nurse dictations is labor-intensive and error-prone, yet it contains high-value observations that must be transferred into structured flowsheets. The MEDIQA-SYNUR 2026 shared task evaluates systems that extract and ontology-align 193 clinical concepts (with heterogeneous value types) from synthetic speech transcripts derived from intensive care notes. We describe the Consensus Adjudication Ensemble (ACE), a three-stage pipeline that (i) maximizes candidate coverage via complementary generators, (ii) enforces high precision through a dedicated adjudicator that operates as a verifier rather than a generator, and (iii) restores strict schema compliance using a targeted, token-efficient repair step. On the official test set we achieve an exact-match micro-F1 of 0.7996 (P=0.7812, R=0.8188), ranking 4th on the leaderboard. Beyond the competitive result, we analyze clinically relevant failure modes - hallucinated interventions, over-confident categorical labels, and unit/normalization errors - and quantify adjudication trade-offs: 2,219 candidates removed, 91.3{\%} of which are true false positives, at the cost of 8.7{\%} mistakenly removed true positives. Finally, targeted schema repair reduces validation context from approx. 230k tokens to {\ensuremath{<}}2k per document while preserving most extraction gains. Keywords: clinical information extraction, nurse dictations, ontology alignment, ensemble methods, adjudication, error analysis"
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<abstract>Clinical documentation from nurse dictations is labor-intensive and error-prone, yet it contains high-value observations that must be transferred into structured flowsheets. The MEDIQA-SYNUR 2026 shared task evaluates systems that extract and ontology-align 193 clinical concepts (with heterogeneous value types) from synthetic speech transcripts derived from intensive care notes. We describe the Consensus Adjudication Ensemble (ACE), a three-stage pipeline that (i) maximizes candidate coverage via complementary generators, (ii) enforces high precision through a dedicated adjudicator that operates as a verifier rather than a generator, and (iii) restores strict schema compliance using a targeted, token-efficient repair step. On the official test set we achieve an exact-match micro-F1 of 0.7996 (P=0.7812, R=0.8188), ranking 4th on the leaderboard. Beyond the competitive result, we analyze clinically relevant failure modes - hallucinated interventions, over-confident categorical labels, and unit/normalization errors - and quantify adjudication trade-offs: 2,219 candidates removed, 91.3% of which are true false positives, at the cost of 8.7% mistakenly removed true positives. Finally, targeted schema repair reduces validation context from approx. 230k tokens to \ensuremath<2k per document while preserving most extraction gains. Keywords: clinical information extraction, nurse dictations, ontology alignment, ensemble methods, adjudication, error analysis</abstract>
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%0 Conference Proceedings
%T HSE NLP TEAM at MEDIQA-SYNUR 2026: Consensus Adjudication Ensemble (ACE): Balancing Precision and Recall for Schema-Bystander Clinical Extraction
%A Valiev, Airat A.
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F valiev-2026-hse
%X Clinical documentation from nurse dictations is labor-intensive and error-prone, yet it contains high-value observations that must be transferred into structured flowsheets. The MEDIQA-SYNUR 2026 shared task evaluates systems that extract and ontology-align 193 clinical concepts (with heterogeneous value types) from synthetic speech transcripts derived from intensive care notes. We describe the Consensus Adjudication Ensemble (ACE), a three-stage pipeline that (i) maximizes candidate coverage via complementary generators, (ii) enforces high precision through a dedicated adjudicator that operates as a verifier rather than a generator, and (iii) restores strict schema compliance using a targeted, token-efficient repair step. On the official test set we achieve an exact-match micro-F1 of 0.7996 (P=0.7812, R=0.8188), ranking 4th on the leaderboard. Beyond the competitive result, we analyze clinically relevant failure modes - hallucinated interventions, over-confident categorical labels, and unit/normalization errors - and quantify adjudication trade-offs: 2,219 candidates removed, 91.3% of which are true false positives, at the cost of 8.7% mistakenly removed true positives. Finally, targeted schema repair reduces validation context from approx. 230k tokens to \ensuremath<2k per document while preserving most extraction gains. Keywords: clinical information extraction, nurse dictations, ontology alignment, ensemble methods, adjudication, error analysis
%R 10.63317/4vsjfsrfx246
%U https://aclanthology.org/2026.clinicalnlp-1.22/
%U https://doi.org/10.63317/4vsjfsrfx246
%P 200-211
Markdown (Informal)
[HSE NLP TEAM at MEDIQA-SYNUR 2026: Consensus Adjudication Ensemble (ACE): Balancing Precision and Recall for Schema-Bystander Clinical Extraction](https://aclanthology.org/2026.clinicalnlp-1.22/) (Valiev, ClinicalNLP 2026)
ACL