Local Explanations for Clinical Search Engine results

TL;DR

This paper introduces a model-agnostic explainable method for clinical search engines that enhances transparency and trust by providing layman-friendly explanations and relevance scores for retrieved clinical trials.

Contribution

It develops a novel explainability approach using knowledge graphs and crowd-sourcing to clarify why trials are retrieved, improving trust for healthcare professionals.

Findings

The method increases trust among medical professionals.

It provides clear, layman-friendly explanations.

It effectively ranks clinical trials based on explainability scores.

Abstract

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.