论文标题
医学抄写员：语料库的发展和模型绩效分析
The Medical Scribe: Corpus Development and Model Performance Analyses
论文作者
论文摘要
人们对创建工具的兴趣越来越多，以使用提供者的会员相遇的音频来协助临床笔记生成。在这个目标和提供者和医学抄写员的帮助下，我们开发了一个注释计划来提取相关的临床概念。我们使用此注释方案将大约6K临床相遇的语料库标记。这用于训练最先进的标签模型。我们报告了本体论，标签结果，模型性能以及结果的详细分析。我们的结果表明，与药物有关的实体可以以相对较高的精度为0.90 f-评分提取，其次是0.72 f-评分的症状，并且条件为0.57 f-SCORE。在我们的任务中，我们不仅确定了提到症状的位置，而且还将其映射到临床注释中出现的规范形式。在大约19-38％的情况下，不同类型的错误中，我们发现模型输出是正确的，约有17-32％的错误不会影响临床注意事项。综上所述，这项工作中开发的模型比F评分所反映的更有用，这使其成为实用应用的有前途的方法。
There is a growing interest in creating tools to assist in clinical note generation using the audio of provider-patient encounters. Motivated by this goal and with the help of providers and medical scribes, we developed an annotation scheme to extract relevant clinical concepts. We used this annotation scheme to label a corpus of about 6k clinical encounters. This was used to train a state-of-the-art tagging model. We report ontologies, labeling results, model performances, and detailed analyses of the results. Our results show that the entities related to medications can be extracted with a relatively high accuracy of 0.90 F-score, followed by symptoms at 0.72 F-score, and conditions at 0.57 F-score. In our task, we not only identify where the symptoms are mentioned but also map them to canonical forms as they appear in the clinical notes. Of the different types of errors, in about 19-38% of the cases, we find that the model output was correct, and about 17-32% of the errors do not impact the clinical note. Taken together, the models developed in this work are more useful than the F-scores reflect, making it a promising approach for practical applications.
