Peter D Stetson, Stephen B Johnson, Matthew Scotch, George Hripcsak
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引用次数: 0
摘要
在哥伦比亚长老会医疗中心,临床医生为了交叉覆盖的目的,将免费文本的“签到”记录输入电子记录。我们计划在随后的项目中使用自然语言处理(NLP)“解锁”这些笔记中包含的有关不良事件的信息。为了更好地理解解析需求,我们将Signout笔记与其他常见医疗笔记(门诊笔记和出院摘要)在一系列定量指标上进行了比较。他们较短(平均长度59.25个字,门诊和出院记录分别为144.11和340.85个字),使用缩写较多(26.88%,20.07%和3.57%)。尽管更简洁,但Signout笔记使用的歧义缩写较少(8.34% vs. 9.09%和18.02%)。采用相对熵和平方卡方距离的新方法对这些医学语料库进行比较,发现了差异。签到笔记似乎构成了一种独特的医学次语言。讨论了将自由文本交叉覆盖笔记解析为编码医疗数据的含义。
At Columbia-Presbyterian Medical Center, free-text "Signout" notes are typed into the electronic record by clinicians for the purpose of cross-coverage. We plan to "unlock" information about adverse events contained in these notes in a subsequent project using Natural Language Processing (NLP). To better understand the requirements for parsing, Signout notes were compared to other common medical notes (ambulatory clinic notes and discharge summaries) on a series of quantitative metrics. They are shorter (mean length 59.25 words vs. 144.11 and 340.85 for ambulatory and discharge notes respectively) and use more abbreviations (26.88% vs. 20.07% and 3.57%). Despite being terser, Signout notes use less ambiguous abbreviations (8.34% vs. 9.09% and 18.02%). Differences were found using Relative Entropy and Squared Chi-square Distance in a novel fashion to compare these medical corpora. Signout notes appear to constitute a unique sublanguage of medicine. The implications for parsing free-text cross-coverage notes into coded medical data are discussed.