A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ such methods for educational purposes. It is therefore necessary to develop new frameworks to enhance explainability in these solutions. In this paper, we present a novel full pipeline to generate automatically natural language explanations for medical diagnoses. The proposed solution starts from a clinical case description associated with a list of correct and incorrect diagnoses and, through the extraction of the relevant symptoms and findings, enriches the information contained in the description with verified medical knowledge from an ontology. Finally, the system returns a pattern-based explanation in natural language which elucidates why the correct (incorrect) diagnosis is the correct (incorrect) one. The main contribution of the paper is twofold: first, we propose two novel linguistic resources for the medical domain (i.e, a dataset of 314 clinical cases annotated with the medical entities from UMLS, and a database of biological boundaries for common findings), and second, a full Information Extraction pipeline to extract symptoms and findings from the clinical cases and match them with the terms in a medical ontology and to the biological boundaries. An extensive evaluation of the proposed approach shows the our method outperforms comparable approaches. Our goal is to offer AI-assisted educational support framework to form clinical residents to formulate sound and exhaustive explanations for their diagnoses to patients.

中文翻译：

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用自然语言解释正确和错误的医学诊断


如今，人工智能领域进行了大量研究，提出了分析医疗数据的自动化方法，旨在支持医生提供医疗诊断。然而，这些方法的一个主要问题是所取得的结果缺乏透明度和可解释性，使得这些方法很难用于教育目的。因此，有必要开发新的框架来增强这些解决方案的可解释性。在本文中，我们提出了一种新颖的完整管道，可以自动生成医疗诊断的自然语言解释。所提出的解决方案从与正确和错误诊断列表相关的临床病例描述开始，通过提取相关症状和发现，用本体中经过验证的医学知识丰富描述中包含的信息。最后，系统以自然语言返回基于模式的解释，阐明为什么正确（不正确）的诊断是正确（不正确）的。本文的主要贡献有两个：首先，我们为医学领域提出了两种新颖的语言资源（即用 UMLS 的医学实体注释的 314 个临床病例的数据集，以及常见发现的生物边界数据库），以及其次，完整的信息提取管道，用于从临床病例中提取症状和发现，并将其与医学本体中的术语和生物学边界相匹配。对所提出的方法的广泛评估表明，我们的方法优于同类方法。我们的目标是提供人工智能辅助的教育支持框架，帮助临床住院医生为患者的诊断制定合理而详尽的解释。