Fette Georg, Krug Markus, Kaspar Mathias, Liman Leon, Dietrich Georg, Ertl Maximilian, Krebs Jonathan, Störk Stefan, Puppe Frank
Würzburg University, Computer Science 6.
University Hospital of Würzburg, Comprehensive Heart Failure Center.
Stud Health Technol Inform. 2018;247:141-145.
ICD encoded diagnoses are a popular criterion for eligibility algorithms for study cohort recruitment. However, "official" ICD encoded diagnoses used for billing purposes are afflicted with a bias originating from legal issues. This work presents an approach to estimate the degree of the encoding bias for the complete ICD catalogue at a German university hospital. The free text diagnoses sections of discharge letters are automatically classified using a supervised machine learning algorithm. The automatic classifications are compared with the official, manually classified codes. For selected ICD codes the approach works sufficiently well.
国际疾病分类（ICD）编码诊断是研究队列招募资格算法中常用的标准。然而，用于计费目的的“官方”ICD编码诊断存在源于法律问题的偏差。这项工作提出了一种方法来估计德国一家大学医院完整ICD目录的编码偏差程度。出院小结的自由文本诊断部分使用监督机器学习算法进行自动分类。将自动分类结果与官方手动分类代码进行比较。对于选定的ICD代码，该方法效果良好。
