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.

翻译：使用国际标准诊断分类体系进行病态数据编码的重要性日益凸显，但其实现仍面临挑战。临床编码员和医师基于对病历摘要或电子病历记录的理解，为患者诊疗事件分配编码。因此，编码的准确性既取决于病历文档的清晰可读性，也依赖于编码员对医学术语的掌握程度。过去十年间，大量研究表明临床编码的可重复性较差，这一现象即使在近期应用基于人工智能模型的辅助编码系统中依然存在。在此背景下，本文旨在介绍SISCO.web方案，该方案旨在辅助医师使用国际疾病分类（第九版和第十版）为出院记录填写正确的诊断与操作编码，其核心功能在于识别主要病理状况。该网络服务利用自然语言处理算法、特定编码规则以及定制化决策树来识别主要病情，在提供准确的国际疾病分类编码建议方面展现出良好前景。