CardioLM：面向心脏病学的多语言小模型套件发布
CardioLM: A multilingual suite of small language models for the cardiology domain
欧洲团队做了心脏病方向的多语言医疗模型，508 份病例扩成 7 种语言语料，结果挺有意思：专业预训练没用，多语言联合训练反而最强。
CardioCCC 语料库包含 508 份西班牙语心脏病病例报告，经机器翻译和临床医生审核扩展至捷克语、荷兰语、英语、意大利语、罗马尼亚语和瑞典语，标注覆盖疾病、症状、操作和药物。基于该语料训练的 CardioNER 与多语言 XLM-RoBERTa-large 模型在几乎所有语言和实体类型上取得最佳严格 F1，比最强单语系统最高超出 0.13。心脏病学专属预训练最多将 pseudo-perplexity 降低 8 倍，但严格 span F1 变化不超过 0.04，说明专业预训练并非决定因素。临床医生评分显示 CardioNER 平均从基线的 26% 提升至 73%。
CardioLM: A multilingual suite of small language models for the cardiology domain
Background: Cardiovascular care generates large volumes of clinical free-text documentation in local languages. However, clinical natural language processing (NLP) resources remain concentrated in English, and no existing benchmark is cardiology-specific, multilingual, and broad in entity coverage. Methods: We present two contributions. The first is CardioCCC, a corpus of 508 cardiology case reports originally published in Spanish and extended to Czech, Dutch, English, Italian, Romanian, and Swedish through machine translation, annotation projection, and clinician review, with annotations covering diseases, symptoms, procedures, and medications. The second is CardioLM, comprising CardioBERTa, a set of language-specific encoders obtained by continued pretraining on cardiology and biomedical corpora, and CardioNER, a set of named entity recognition (NER) models fine-tuned on CardioCCC. Results: Cardiology-specific pretraining reduced pseudo-perplexity by up to a factor of eight, yet changed strict span-level F1 by at most 0.04, with benefits concentrated in languages whose backbones lacked biomedical coverage. The jointly trained multilingual XLM-RoBERTa-large models achieved the best strict F1 in nearly every language and entity type, exceeding the strongest monolingual system by up to 0.13 F1. A large share of residual errors concerned boundary detection: relaxed matching recovered 0.10-0.23 F1 for disease, procedure, and symptom, but only 0.01-0.07 for medication. Clinicians rated CardioNER well above a rule- and dictionary-based baseline, with the mean rating across entity types rising from 26% to 73%. Conclusions: Specialty-specific pretraining is not the decisive factor for downstream entity recognition, whereas multilingual joint training yields the strongest results in nearly all settings and a simpler deployment path for multi-country clinical information extraction.