Anna Markella Antoniadi

Sense of belonging, or belongingness, is an important factor in a student’s academic life as it is associated with factors such as motivation and persistence, in addition to mental health and well-being. Belongingness has been shown to be correlated with factors such as gender, race, nationality, socio-economic status, religion and disability. Previous work has shown lower belongingness of minoritised women in computing compared to other computing students. However, it is not well known how belongingness in computing compares to other disciplines. This is a difficult comparison to draw as belongingness is closely related to specific contexts including learning environment, and therefore one’s discipline. We utilised a survey adapted from the validated “Math Sense of Belonging Scale” to examine the belongingness of undergraduate science – including computer science – students. We examined how the belongingness of students varies by gender identity and minoritisation, how the belongingness of computer science students compares to that of other science students, and how the belongingness of students in computer science varies by gender identity and minoritisation compared to other science students. In this specific study we found that belongingness is not statistically significantly different across genders overall. However, minoritised students have statistically significantly lower belongingness, which varies by gender. We also found that the belongingness of computer science students is statistically significantly lower than that of other science students and that belongingness varies across science disciplines by gender and minoritisation. Our results provide insight that may help us as well as others improve the belongingness of undergraduate computer science students, with a particular focus on the intersection of identities. We will explore the impact of interventions to increase belongingness in future work.

Explainable artificial intelligence (XAI) has shown benefits in clinical decision support systems (CDSSs); however, it is still unclear to CDSS developers how to select an XAI method to optimize the advice-taking of healthcare practitioners. We performed a user study on healthcare practitioners based on a machine learning-based CDSS for the prediction of gestational diabetes mellitus to explore and compare two XAI methods: explanation by feature contribution and explanation by example. Participants were asked to make estimates for both correctly and incorrectly predicted cases to determine if there were any over-reliance or self-reliance issues. We examined the weight of advice and healthcare practitioners’ preferences. Our results based on statistical tests showed no significant difference between the two XAI methods regarding the advice-taking. The CDSS explained by either method had a substantial impact on the decision-making of healthcare practitioners; however, both methods may lead to over-reliance issues. We identified the inclination towards CDSS use as a key factor in the advice-taking from an explainable CDSS among obstetricians. Additionally, we found that different types of healthcare practitioners had differing preferences for explanations; therefore, we suggest that CDSS developers should select XAI methods according to their target users.

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 post-hoc 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.

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Objectives Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that is characterised by the rapid degeneration of upper and lower motor neurons and has a fatal trajectory 3–4 years from symptom onset. Due to the nature of the condition patients with ALS require the assistance of informal caregivers whose task is demanding and can lead to high feelings of burden. This study aims to predict caregiver burden and identify related features using machine learning techniques. Design This included demographic and socioeconomic information, quality of life, anxiety and depression questionnaires, for patients and carers, resource use of patients and clinical information. The method used for prediction was the Random forest algorithm. Setting and participants This study investigates a cohort of 90 patients and their primary caregiver at three different time-points. The patients were attending the National ALS/Motor Neuron Disease Multidisciplinary Clinic at Beaumont Hospital, Dublin. Results The caregiver’s quality of life and psychological distress were the most predictive features of burden (0.92 sensitivity and 0.78 specificity). The most predictive features for Clinical Decision Support model were associated with the weekly caregiving duties of the primary caregiver as well as their age and health and also the patient’s physical functioning and age of onset. However, this model had a lower sensitivity and specificity score (0.84 and 0.72, respectively). The ability of patients without gastrostomy to cut food and handle utensils was also highly predictive of burden in this study. Generally, our models are better in predicting the high-risk category, and we suggest that information related to the caregiver’s quality of life and psychological distress is required. Conclusion This work demonstrates a proof of concept of an informatics solution to identifying caregivers at risk of burden that could be incorporated into future care pathways.