Objective: To develop a natural language processing (NLP) system to extract
medications and contextual information that help understand drug changes. This
project is part of the 2022 n2c2 challenge.
Materials and methods: We developed NLP systems for medication mention
extraction, event classification (indicating medication changes discussed or
not), and context classification to classify medication changes context into 5
orthogonal dimensions related to drug changes. We explored 6 state-of-the-art
pretrained transformer models for the three subtasks, including GatorTron, a
large language model pretrained using >90 billion words of text (including >80
billion words from >290 million clinical notes identified at the University of
Florida Health). We evaluated our NLP systems using annotated data and
evaluation scripts provided by the 2022 n2c2 organizers.
Results:Our GatorTron models achieved the best F1-scores of 0.9828 for
medication extraction (ranked 3rd), 0.9379 for event classification (ranked
2nd), and the best micro-average accuracy of 0.9126 for context classification.
GatorTron outperformed existing transformer models pretrained using smaller
general English text and clinical text corpora, indicating the advantage of
large language models.
Conclusion: This study demonstrated the advantage of using large transformer
models for contextual medication information extraction from clinical
narratives.

研究开发了自然语言处理（NLP）系统，用于从临床叙述中提取上下文药物信息，证明大型转换模型的优势。

基于 Transformer 深度学习架构的上下文化药物信息提取

Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures

Natural language understanding (NLU) has made massive progress driven by
large benchmarks, but benchmarks often leave a long tail of infrequent
phenomena underrepresented. We reflect on the question: have transfer learning
methods sufficiently addressed the poor performance of benchmark-trained models
on the long tail? We conceptualize the long tail using macro-level dimensions
(e.g., underrepresented genres, topics, etc.), and perform a qualitative
meta-analysis of 100 representative papers on transfer learning research for
NLU. Our analysis asks three questions: (i) Which long tail dimensions do
transfer learning studies target? (ii) Which properties of adaptation methods
help improve performance on the long tail? (iii) Which methodological gaps have
greatest negative impact on long tail performance? Our answers highlight major
avenues for future research in transfer learning for the long tail. Lastly,
using our meta-analysis framework, we perform a case study comparing the
performance of various adaptation methods on clinical narratives, which
provides interesting insights that may enable us to make progress along these
future avenues.

对自然语言理解中基于转移学习的长尾现象进行了定量的元分析研究，较全面地探讨了转移学习方法在改善此类现象上的可行性问题和方法论实现问题，并在基于临床叙述的案例研究中比较了多种适应性方法的性能表现。

适应长尾：迁移学习研究在语言理解任务上的元分析

Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research  for Language Understanding Tasks

Summarization of clinical narratives is a long-standing research problem.
Here, we introduce the task of hospital-course summarization. Given the
documentation authored throughout a patient's hospitalization, generate a
paragraph that tells the story of the patient admission. We construct an
English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and
their corresponding summary proxy: the clinician-authored "Brief Hospital
Course" paragraph written as part of a discharge note. Exploratory analyses
reveal that the BHC paragraphs are highly abstractive with some long extracted
fragments; are concise yet comprehensive; differ in style and content
organization from the source notes; exhibit minimal lexical cohesion; and
represent silver-standard references. Our analysis identifies multiple
implications for modeling this complex, multi-document summarization task.

介绍了医院纪录摘要的任务，提出了构建一个由 109,000 个入院病例的医师所写的 “简短住院经过” 段落和相应摘要代理构成的英文文本数据集，并通过实证分析确认其高抽象性、简洁全面性、不同的组织风格，是模拟这种复杂的、多文档摘要任务的重要实例。

摘要中包含什么？为医院课程摘要进步奠定基础

What's in a Summary? Laying the Groundwork for Advances in  Hospital-Course Summarization

Electronic health records capture patient information using structured
controlled vocabularies and unstructured narrative text. While structured data
typically encodes lab values, encounters and medication lists, unstructured
data captures the physician's interpretation of the patient's condition,
prognosis, and response to therapeutic intervention. In this paper, we
demonstrate that information extraction from unstructured clinical narratives
is essential to most clinical applications. We perform an empirical study to
validate the argument and show that structured data alone is insufficient in
resolving eligibility criteria for recruiting patients onto clinical trials for
chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is
essential to solving 59% of the CLL trial criteria and 77% of the prostate
cancer trial criteria. More specifically, for resolving eligibility criteria
with temporal constraints, we show the need for temporal reasoning and
information integration with medical events within and across unstructured
clinical narratives and structured data.

结构化电子健康记录和非结构化临床叙述是诊疗过程中不可或缺的信息来源，我们进行的实证研究表明，从非结构化临床叙述中提取信息对于大多数诊疗应用来说是必不可少的。