Antoniadi Anna Markella, Galvin Miriam, Heverin Mark, Wei Lan, Hardiman Orla, Mooney Catherine
UCD School of Computer Science, University College Dublin, Dublin 4, Ireland.
FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.
J Pers Med. 2022 Mar 10;12(3):435. doi: 10.3390/jpm12030435.
Amyotrophic Lateral Sclerosis (ALS), also known as Motor Neuron Disease (MND), is a rare and fatal neurodegenerative disease. As ALS is currently incurable, the aim of the treatment is mainly to alleviate symptoms and improve quality of life (QoL). We designed a prototype Clinical Decision Support System (CDSS) to alert clinicians when a person with ALS is experiencing low QoL in order to inform and personalise the support they receive. Explainability is important for the success of a CDSS and its acceptance by healthcare professionals. The aim of this work isto announce our prototype (C-ALS), supported by a first short evaluation of its explainability. Given the lack of similar studies and systems, this work is a valid proof-of-concept that will lead to future work. We developed a CDSS that was evaluated by members of the team of healthcare professionals that provide care to people with ALS in the ALS/MND Multidisciplinary Clinic in Dublin, Ireland. We conducted a user study where participants were asked to review the CDSS and complete a short survey with a focus on explainability. Healthcare professionals demonstrated some uncertainty in understanding the system's output. Based on their feedback, we altered the explanation provided in the updated version of our CDSS. C-ALS provides local explanations of its predictions in a manner, using SHAP (SHapley Additive exPlanations). The CDSS predicts the risk of low QoL in the form of a probability, a bar plot shows the feature importance for the specific prediction, along with some verbal guidelines on how to interpret the results. Additionally, we provide the option of a global explanation of the system's function in the form of a bar plot showing the average importance of each feature. C-ALS is available online for academic use.
肌萎缩侧索硬化症（ALS），也被称为运动神经元病（MND），是一种罕见的致命性神经退行性疾病。由于ALS目前无法治愈，治疗的目的主要是缓解症状并提高生活质量（QoL）。我们设计了一个临床决策支持系统（CDSS）原型，以便在ALS患者生活质量较低时提醒临床医生，从而为他们提供的支持提供信息并实现个性化。可解释性对于CDSS的成功及其被医疗保健专业人员接受至关重要。这项工作的目的是公布我们的原型（C-ALS），并对其可解释性进行首次简短评估。鉴于缺乏类似的研究和系统，这项工作是一个有效的概念验证，将引领未来的工作。我们开发了一个CDSS，由爱尔兰都柏林ALS/MND多学科诊所中为ALS患者提供护理的医疗保健专业团队成员进行评估。我们进行了一项用户研究，要求参与者审查CDSS并完成一项侧重于可解释性的简短调查。医疗保健专业人员在理解系统输出方面表现出一些不确定性。根据他们的反馈，我们更改了CDSS更新版本中提供的解释。C-ALS使用SHAP（SHapley Additive exPlanations）以一种方式提供其预测的局部解释。CDSS以概率的形式预测生活质量低的风险，柱状图显示特定预测的特征重要性，以及一些关于如何解释结果的文字指南。此外，我们提供以柱状图形式对系统功能进行全局解释的选项，该柱状图显示每个特征的平均重要性。C-ALS可在线供学术使用。
