SODA：一款用于癌症研究中提取健康社会决定因素的自然语言处理工具包
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
AI总结：
本研究开发了搭载预训练Transformer模型的开源NLP工具包SODA，用于提取癌症患者的健康社会决定因素，验证了其在阿片类药物使用领域的泛化能力，并在多类癌症队列中评估了提取率，工具包已公开。
摘要：
研究目的：我们旨在开发一款名为SODA（即SOcial DeterminAnts，健康社会决定因素提取工具）的开源自然语言处理（NLP）工具包，搭载预训练Transformer模型，用于提取癌症患者的健康社会决定因素（SDoH），检验SODA在新疾病领域（即阿片类药物使用）的泛化能力，并评估其在癌症人群中的SDoH提取率。\n研究方法：我们确定了SDoH的类别与属性，利用普通癌症队列的临床病历构建了SDoH语料库。我们对比了4种基于Transformer的NLP模型的SDoH提取效果，检验了这些NLP模型在接受阿片类药物处方的患者队列中的泛化能力，并探索了提升性能的定制化策略。我们将表现最优的NLP模型应用于乳腺癌（n=7971）、肺癌（n=11804）和结直肠癌（n=6240）队列，提取19类SDoH。\n结果与结论：我们构建了包含629份癌症患者病历的语料库，标注了来自19类SDoH的13193个SDoH概念/属性。双向编码器表征Transformer（BERT）模型表现最优，在SDoH概念提取任务上的严格/宽松F1值分别为0.9216和0.9441，在属性与SDoH概念关联任务上的严格/宽松F1值分别为0.9617和0.9626。使用阿片类药物使用患者的新标注数据微调NLP模型后，严格/宽松F1值从0.8172/0.8502提升至0.8312/0.8679。19类SDoH的提取率差异较大，其中10类SDoH可从超过70%的癌症患者病历中提取，而9类SDoH提取率较低（不足70%的癌症患者）。搭载预训练Transformer模型的SODA工具包已在https://github.com/uf-hobiinformatics-lab/SDoH_SODA公开。
英文摘要：
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i.e., opioid use), and evaluate the extraction rate of SDoH using cancer populations. Methods: We identified SDoH categories and attributes and developed an SDoH corpus using clinical notes from a general cancer cohort. We compared four transformer-based NLP models to extract SDoH, examined the generalizability of NLP models to a cohort of patients prescribed with opioids, and explored customization strategies to improve performance. We applied the best NLP model to extract 19 categories of SDoH from the breast (n=7,971), lung (n=11,804), and colorectal cancer (n=6,240) cohorts. Results and Conclusion: We developed a corpus of 629 cancer patients notes with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH. The Bidirectional Encoder Representations from Transformers (BERT) model achieved the best strict/lenient F1 scores of 0.9216 and 0.9441 for SDoH concept extraction, 0.9617 and 0.9626 for linking attributes to SDoH concepts. Fine-tuning the NLP models using new annotations from opioid use patients improved the strict/lenient F1 scores from 0.8172/0.8502 to 0.8312/0.8679. The extraction rates among 19 categories of SDoH varied greatly, where 10 SDoH could be extracted from >70% of cancer patients, but 9 SDoH had a low extraction rate (<70% of cancer patients). The SODA package with pre-trained transformer models is publicly available at https://github.com/uf-hobiinformatics-lab/SDoH_SODA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
