用AI辅助医院出院记录主诊断编码，提升准确性与一致性。
Assisted morbidity coding: the SISCO.web use case for identifying the main diagnosis in Hospital Discharge Records
- 基于NLP和规则决策树识别主诊断
- 在ICD-9/ICD-10标准下提供高准确率编码建议
- 适合临床医生和编码员提升编码效率
使用国际疾病分类标准进行疾病数据编码日益重要但依然困难。临床医生和编码人员需根据病历或电子病历内容判断并赋予诊断代码，其准确性依赖于病历可读性及对医学术语的理解。过去十年的研究表明，即使采用基于人工智能的模型，临床编码仍存在较差的可重复性。本文提出SISCO.web方法，旨在通过自然语言处理算法、特定编码规则和定制决策树，帮助医生在出院记录中正确填写诊断与操作代码，尤其聚焦于识别主要病理状况。该网络服务在提供符合ICD-9/ICD-10标准的编码建议方面表现出良好效果。
原文摘要 · Abstract (English)
Coding morbidity data using international standard diagnostic classifications is increasingly important and still challenging. Clinical coders and physicians assign codes to patient episodes based on their interpretation of case notes or electronic patient records. Therefore, accurate coding relies on the legibility of case notes and the coders' understanding of medical terminology. During the last ten years, many studies have shown poor reproducibility of clinical coding, even recently, with the application of Artificial Intelligence-based models. Given this context, the paper aims to present the SISCO.web approach designed to support physicians in filling in Hospital Discharge Records with proper diagnoses and procedures codes using the International Classification of Diseases (9th and 10th), and, above all, in identifying the main pathological condition. The web service leverages NLP algorithms, specific coding rules, as well as ad hoc decision trees to identify the main condition, showing promising results in providing accurate ICD coding suggestions.
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