过去几十年的数字化:非结构化自由文本皮肤病诊断的自动ICD-10编码
Sebastian Sitaru1, Fabian Nhan2, Christine Gasteiger2
1School of Medicine and Health, Department of Dermatology and Allergy, Technical University of Munich, Biedersteiner Str. 29, Munich, 80802, Germany. sebastian.sitaru@tum.de.
这项研究开发了一种基于规则的算法,将手写的德国诊断与ICD-10代码进行映射,达到82%的准确性. 这使得可以分析遗留的医疗数据,通过访问历史医学知识来改善患者护理.
科学领域:
- 皮肤病学 皮肤病学
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 传统的医疗数据库通常包含非结构化,自由文本诊断,需要将其映射到标准化编码系统进行分析.
- 现有的方法难以处理完全非结构化的自由文本诊断,没有额外的患者数据.
研究的目的:
- 开发和验证一种算法,将手写的德国皮肤病诊断与ICD-10代码进行映射.
- 展示算法在分析历史诊断数据和识别趋势方面的实用性.
主要方法:
- 创建了一个基于规则的算法来处理来自临床摄影数据库的50,884个手写的德国诊断.
- 该算法的输出由皮肤科医生对817个诊断进行了验证.
- 随着时间的推移对诊断计数的分析作为概念验证进行.
主要成果:
- 该算法成功地将诊断映射到ICD-10代码,准确度为82%.
- 首要确定的诊断包括牛皮,湿疹和非黑色素瘤皮肤癌.
- 在慢性炎症疾病诊断中观察到季节性模式和与治疗进展的相关性.
结论:
- 开发的算法可靠地将手写的德国皮肤病诊断与ICD-10代码进行映射.
- 这有助于对传统数据库进行系统分析,为当前的患者护理解锁历史医学知识.
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