Intelligent Data-Driven Model for Diabetes Diurnal Patterns Analysis

Participants' experiences of attending a structured education course (DAFNEplus) informed by behavioural science

AIMS

As part of a broader process evaluation, we explored participants' experiences of, and engagement with, the DAFNEplus programme's group-based structured education course. This course, which was informed by behavioural science, provided participants with education and instruction to use flexible intensive insulin therapy (FIIT) together with techniques to identify and address unhelpful cognitive and emotional influences on their type 1 diabetes self-management.

METHODS

We interviewed n = 28 DAFNEplus participants. Data were analysed thematically and took account of previous work exploring individuals' experiences of standard DAFNE courses.

RESULTS

As well as benefitting from the DAFNEplus course's skills-based training and educational curriculum, participants' accounts suggested they had experienced cognitive and emotional changes that had positively influenced their confidence and motivation to adopt and sustain the use of FIIT. These benefits were most keenly felt by those who reported negative emotional states and mind-sets pre-course which had made their diabetes self-management challenging. Participants' cognitive and emotional changes were enabled through techniques used during the course to normalise setbacks and imperfect diabetes self-management, capitalise upon group synergies and encourage the use of social support, including from healthcare professionals. Participants also highlighted motivational gains arising from being reassured that diabetes complications are not common or inevitable if a FIIT regimen is followed.

CONCLUSIONS

Our findings suggest that offering training in FIIT, in conjunction with behaviour change techniques that target unhelpful mindsets and emotional resilience, may be more effective in promoting diabetes self-management than offering education and skills training alone.

Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study

Background

Hypoglycemia is the most common adverse consequence of treating diabetes, and is often due to suboptimal patient self-care. Behavioral interventions by health professionals and self-care education helps avoid recurrent hypoglycemic episodes by targeting problematic patient behaviors. This relies on time-consuming investigation of reasons behind the observed episodes, which involves manual interpretation of personal diabetes diaries and communication with patients. Therefore, there is a clear motivation to automate this process using a supervised machine learning paradigm. This manuscript presents a feasibility study of automatic identification of hypoglycemia causes.

Methods

Reasons for 1885 hypoglycemia events were labeled by 54 participants with type 1 diabetes over a 21 months period. A broad range of possible predictors were extracted describing a hypoglycemic episode and the subject's general self-care from participants' routinely collected data on the Glucollector, their diabetes management platform. Thereafter, the possible hypoglycemia reasons were categorized for two major analysis sections - statistical analysis of relationships between the data features of self-care and hypoglycemia reasons, and classification analysis investigating the design of an automated system to determine the reason for hypoglycemia.

Results

Physical activity contributed to 45% of hypoglycemia reasons on the real world collected data. The statistical analysis provided a number of interpretable predictors of different hypoglycemia reasons based on self-care behaviors. The classification analysis showed the performance of a reasoning system in practical settings with different objectives under F1-score, recall and precision metrics.

Conclusion

The data acquisition characterized the incidence distribution of the various hypoglycemia reasons. The analyses highlighted many interpretable predictors of the various hypoglycemia types. Also, the feasibility study presented a number of concerns valuable in the design of the decision support system for automatic hypoglycemia reason classification. Therefore, automating the identification of the causes of hypoglycemia may help objectively to target behavioral and therapeutic changes in patients' care.

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.