临床试验潜在核心结果的自动化汇编
Automating the Compilation of Potential Core-Outcomes for Clinical Trials
摘要：
由于获得临床试验结果和分析的机会增加，研究人员和科学家能够更有效地改进或改进相关方法。然而，临床试验的指标和相关结果通常在报告中不遵循任何标准化，这使得研究人员更难以解析不同试验的结果。本文的目的是描述一种利用自然语言处理的自动化方法，以描述临床试验的可能的核心结果，以减轻不同的临床试验结果的问题。由于该过程的性质是领域特定的，因此采用BioBERT来进行多类实体规范化任务。除了BioBERT之外，还使用了一种无监督的基于特征的方法，该方法仅使用结果和标签的编码器输出嵌入表示。最后，计算向量间的余弦相似度，得到语义相似度。这种方法能够利用来自BioBERT模型的学习嵌入的每个令牌的特定于域的上下文，以及更稳定的句子相似性度量。汇编了在每个分类中使用Jaccard相似性确定的一些共同结果，虽然有些结果是站不住脚的，但建立了一个可以进行这种自动化过程的管道。
英文摘要：
Due to increased access to clinical trial outcomes and analysis, researchers and scientists are able to iterate or improve upon relevant approaches more effectively. However, the metrics and related results of clinical trials typically do not follow any standardization in their reports, making it more difficult for researchers to parse the results of different trials. The objective of this paper is to describe an automated method utilizing natural language processing in order to describe the probable core outcomes of clinical trials, in order to alleviate the issues around disparate clinical trial outcomes. As the nature of this process is domain specific, BioBERT was employed in order to conduct a multi-class entity normalization task. In addition to BioBERT, an unsupervised feature-based approach making use of only the encoder output embedding representations for the outcomes and labels was utilized. Finally, cosine similarity was calculated across the vectors to obtain the semantic similarity. This method was able to both harness the domain-specific context of each of the tokens from the learned embeddings of the BioBERT model as well as a more stable metric of sentence similarity. Some common outcomes identified using the Jaccard similarity in each of the classifications were compiled, and while some are untenable, a pipeline for which this automation process could be conducted was established.
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。
