Interpretable Deep Learning to Map Diagnostic Texts to ICD10 Codes

International Journal of Medical Informatics(2019)

引用 38|浏览123
暂无评分
摘要
•Automatic extraction of morbid disease or conditions contained in death certificates is extremely useful for standardization, alleviating and smoothing human work. The positive impact of standardization is specially relevant for epidemiological studies, comparison across physicians, hospitals and countries and also for billing purposes.•General and multilingual approach to render diagnostic terms in death certificates into the standard framework provided by the ICD.•Automatic coding of diagnostic terms treated as an automatic translation task.•Study of the impact of different neural architectures on sequence-to-sequence ICD-10 coding.•Our results give a new state of the art on multilingual ICD-10 coding, outperforming several alternative approaches.•Informative ICD-10 coding, interpretable by clinicians.
更多
查看译文
关键词
International Classification of Diseases,Electronic health records,Sequence-to-sequence mapping,Neural machine translation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要