Institute of Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt, Germany.
Department of Informatics, Goethe University Frankfurt, Frankfurt, Germany.
Stud Health Technol Inform. 2022 Jun 29;295:422-425. doi: 10.3233/SHTI220755.
Automated coding of diseases can support hospitals in the billing of inpatient cases with the health insurance funds. This paper describes the implementation and evaluation of classification methods for two selected Rare Diseases. Different classifiers of an off-the-shelf system and an own application are applied in a supervised learning process and comparatively examined for their suitability and reliability. Using Natural Language Processing and Machine Learning, disease entities are recognized from unstructured historical patient records and new billing cases are coded automatically. The results of the performed classifications show that even with small datasets (≤ 200), high correctness (F1 score ∼0.8) can be achieved in predicting new cases.
疾病的自动化编码可以支持医院向医保基金对住院病例进行计费。本文描述了两种选定的罕见病分类方法的实现和评估。在监督学习过程中，应用了现成系统和自主应用的不同分类器，并对其适用性和可靠性进行了比较。利用自然语言处理和机器学习，从非结构化的历史患者记录中识别疾病实体，并自动对新的计费病例进行编码。所进行分类的结果表明，即使在小数据集（≤200）中，也可以实现新病例预测的高准确性（F1 分数≈0.8）。
