  Blood glucose prediction is an important component of biomedical technology
for managing diabetes with automated insulin delivery systems. Machine learning
and deep learning algorithms hold the potential to advance this technology.
However, the lack of standardized methodologies impedes direct comparisons of
emerging algorithms. This study addresses this challenge by developing
GluPredKit, a software platform designed to standardize the training, testing,
and comparison of blood glucose prediction algorithms. GluPredKit features a
modular, open-source architecture, complemented by a command-line interface,
comprehensive documentation, and a video tutorial to enhance usability. To
ensure the platform's effectiveness and user-friendliness, we conducted
preliminary testing and a user study. In this study, four participants
interacted with GluPredKit and provided feedback through the System Usability
Scale (SUS) and open-ended questions. The findings indicate that GluPredKit
effectively addresses the standardization challenge and offers high usability,
facilitating direct comparisons between different algorithms. Additionally, it
serves an educational purpose by making advanced methodologies more accessible.
Future directions include continuously enhancing the software based on user
feedback. We also invite community contributions to further expand GluPredKit
with state-of-the-art components and foster a collaborative effort in
standardizing blood glucose prediction research, leading to more comparable
studies.
