Sarah Clinch

Mohammed Basheikh, Rujiravee Kongdee, Hood Thabit et al.

This study explored healthcare professionals' perspectives on the management of Type 1 Diabetes Mellitus (T1DM) through a two-part questionnaire. The first part examined how clinicians prioritise and apply current clinical guidelines, including the relative importance assigned to different aspects of T1DM management. The second part investigated clinicians' perceptions of patients' ability to interpret data from the glucose monitoring devices and to make appropriate treatment decisions. An online questionnaire was completed by 19 healthcare professionals working in diabetes-related roles in the United Kingdom. The findings revealed that blood glucose management is prioritised within clinical guidance and that advice is frequently tailored to individual patient needs. Additionally, clinicians generally perceive that data presented in glucose monitoring devices is easy for patients to interpret and based on these data, they believe that patients occasionally make correct treatment decisions.

Filippos Ventirozos, Sarah Clinch, Riza Batista-Navarro

Semantic parsing of user-generated instructional text, in the way of enabling end-users to program the Internet of Things (IoT), is an underexplored area. In this study, we provide a unique annotated corpus which aims to support the transformation of cooking recipe instructions to machine-understandable commands for IoT devices in the kitchen. Each of these commands is a tuple capturing the semantics of an instruction involving a kitchen device in terms of "What", "Where", "Why" and "How". Based on this corpus, we developed machine learning-based sequence labelling methods, namely conditional random fields (CRF) and a neural network model, in order to parse recipe instructions and extract our tuples of interest from them. Our results show that while it is feasible to train semantic parsers based on our annotations, most natural-language instructions are incomplete, and thus transforming them into formal meaning representation, is not straightforward.