Computer Science, Unviversity of Würzburg, Am Hubland, Würzburg, 97074, Germany.
Comprehensive Heart Failure Center, University and University Hospital Hospital of Würzburg, Am Schwarzenberg 15, Würzburg, 97078, Germany.
BMC Med Inform Decis Mak. 2019 Jan 18;19(1):15. doi: 10.1186/s12911-018-0729-0.
Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of information extraction (IE) demands a high developmental effort, we used ad hoc IE instead. This technique queries information and extracts it on the fly from texts contained in the CDW.
We present a generalizable approach of ad hoc IE for pharmacotherapy (medications and their daily dosage) presented in hospital discharge letters. We added import and query features to the CDW system, like error tolerant queries to deal with misspellings and proximity search for the extraction of the daily dosage. During the data integration process in the CDW, negated, historical and non-patient context data are filtered. For the replication studies, we used a drug list grouped by ATC (Anatomical Therapeutic Chemical Classification System) codes as input for queries to the CDW.
We achieve an F1 score of 0.983 (precision 0.997, recall 0.970) for extracting medication from discharge letters and an F1 score of 0.974 (precision 0.977, recall 0.972) for extracting the dosage. We replicated three published medical trend studies for hypertension, atrial fibrillation and chronic kidney disease. Overall, 93% of the main findings could be replicated, 68% of sub-findings, and 75% of all findings. One study could be completely replicated with all main and sub-findings.
A novel approach for ad hoc IE is presented. It is very suitable for basic medical texts like discharge letters and finding reports. Ad hoc IE is by definition more limited than conventional IE and does not claim to replace it, but it substantially exceeds the search capabilities of many CDWs and it is convenient to conduct replication studies fast and with high quality.
药物趋势研究显示了多年来药物的变化，并可通过临床数据仓库 (CDW) 进行复制。即使在今天，许多患者信息，如电子病历中的药物数据，仍以自由文本的形式存储。由于传统的信息提取 (IE) 方法需要大量的开发工作，因此我们使用了特定于任务的 IE 方法。该技术从 CDW 中包含的文本中实时查询和提取信息。
我们提出了一种可用于从医院出院记录中提取药物治疗（药物及其每日剂量）信息的特定于任务的 IE 通用方法。我们向 CDW 系统添加了导入和查询功能，例如容错查询来处理拼写错误，以及近邻搜索来提取每日剂量。在 CDW 中的数据集成过程中，过滤了否定、历史和非患者上下文数据。对于复制研究，我们使用按 ATC（解剖治疗化学分类系统）代码分组的药物列表作为查询 CDW 的输入。
我们从出院记录中提取药物的 F1 得分为 0.983（精度 0.997，召回率 0.970），提取剂量的 F1 得分为 0.974（精度 0.977，召回率 0.972）。我们复制了三项已发表的医学趋势研究，分别为高血压、心房颤动和慢性肾病。总体而言，93%的主要发现可以复制，68%的次要发现可以复制，75%的所有发现可以复制。一项研究可以用所有主要和次要发现完全复制。
提出了一种新的特定于任务的 IE 方法。它非常适合基本的医疗文本，如出院记录和发现报告。特定于任务的 IE 从定义上讲比传统的 IE 更有限，并不声称要取代它，但它大大超过了许多 CDW 的搜索能力，并且方便快速、高质量地进行复制研究。
