Health care professionals rely on treatment search engines to efficiently find adequate clinical trials and early access programs for their patients. However, doctors lose trust in the system if its underlying processes are unclear and unexplained. In this paper, a model-agnostic explainable method is developed to provide users with further information regarding the reasons why a clinical trial is retrieved in response to a query. To accomplish this, the engine generates features from clinical trials using by using a knowledge graph, clinical trial data and additional medical resources. and a crowd-sourcing methodology is used to determine their importance. Grounded on the proposed methodology, the rationale behind retrieving the clinical trials is explained in layman's terms so that healthcare processionals can effortlessly perceive them. In addition, we compute an explainability score for each of the retrieved items, according to which the items can be ranked. The experiments validated by medical professionals suggest that the proposed methodology induces trust in targeted as well as in non-targeted users, and provide them with reliable explanations and ranking of retrieved items.
翻译:保健专业人员依靠治疗搜索引擎来有效地为其病人找到适当的临床试验和早期治疗方案。然而,如果其基本过程不明确和无法解释,医生对该系统失去信任。在本文件中,开发了一种示范性、不可知性的解释方法,以便向用户提供进一步资料,说明根据询问检索临床试验的原因。为实现这一目标,引擎利用知识图、临床试验数据和额外医疗资源,从临床试验中产生特征,并使用众包方法确定其重要性。根据拟议方法,对检索临床试验的理由以非专业术语解释,以便医疗过程能够不遗余力地认识这些原理。此外,我们还计算了每个检索到的物品的可解释性分数,据此对物品进行排序。医学专业人员验证的实验表明,拟议的方法可以吸引对目标用户和非目标用户的信任,并向他们提供可靠的解释和检索到物品的等级。