Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

Exploring the progress of artificial intelligence in managing type 2 diabetes mellitus: a comprehensive review of present innovations and anticipated challenges ahead

A significant worldwide health issue, Type 2 Diabetes Mellitus (T2DM) calls for creative solutions. This in-depth review examines the growing severity of T2DM and the requirement for individualized management approaches. It explores the use of artificial intelligence (AI) in the treatment of diabetes, highlighting its potential for diagnosis, customized treatment plans, and patient self-management. The paper highlights the roles played by AI applications such as expert systems, machine learning algorithms, and deep learning approaches in the identification of retinopathy, the interpretation of clinical guidelines, and prediction models. Examined are difficulties with individualized diabetes treatment, including complex technological issues and patient involvement. The review highlights the revolutionary potential of AI in the management of diabetes and calls for a balanced strategy in which AI supports clinical knowledge. It is crucial to pay attention to ethical issues, data privacy, and joint research initiatives.

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Background

Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters.

Objective

The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management.

Methods

A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts.

Results

On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices.

Conclusions

Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Background

Diabetes management is complex, and program personalization has been identified to enhance engagement and clinical outcomes in diabetes management programs. However, 50% of individuals living with diabetes are unable to achieve glycemic control, presenting a gap in the delivery of self-management education and behavior change. Machine learning and recommender systems, which have been used within the health care setting, could be a feasible application for diabetes management programs to provide a personalized user experience and improve user engagement and outcomes.

Objective

This study aims to evaluate machine learning models using member-level engagements to predict improvement in estimated A_1c and develop personalized action recommendations within a remote diabetes monitoring program to improve clinical outcomes.

Methods

A retrospective study of Livongo for Diabetes member engagement data was analyzed within five action categories (interacting with a coach, reading education content, self-monitoring blood glucose level, tracking physical activity, and monitoring nutrition) to build a member-level model to predict if a specific type and level of engagement could lead to improved estimated A_1c for members with type 2 diabetes. Engagement and improvement in estimated A_1c can be correlated; therefore, the doubly robust learning method was used to model the heterogeneous treatment effect of action engagement on improvements in estimated A_1c.

Results

The treatment effect was successfully computed within the five action categories on estimated A_1c reduction for each member. Results show interaction with coaches and self-monitoring blood glucose levels were the actions that resulted in the highest average decrease in estimated A_1c (1.7% and 1.4%, respectively) and were the most recommended actions for 54% of the population. However, these were found to not be the optimal interventions for all members; 46% of members were predicted to have better outcomes with one of the other three interventions. Members who engaged with their recommended actions had on average a 0.8% larger reduction in estimated A_1c than those who did not engage in recommended actions within the first 3 months of the program.

Conclusions

Personalized action recommendations using heterogeneous treatment effects to compute the impact of member actions can reduce estimated A_1c and be a valuable tool for diabetes management programs in encouraging members toward actions to improve clinical outcomes.

Usability of an Intelligent Virtual Assistant for Promoting Behavior Change and Self-Care in Older People with Type 2 Diabetes

In the context of the VASelfCare project, we developed an application prototype of an intelligent anthropomorphic virtual assistant. Designed as a relational agent, the virtual assistant has the role of supporting older people with Type 2 Diabetes Mellitus (T2D) in medication adherence and lifestyle changes. Our paper has two goals: describing the essentials of this prototype, and reporting on usability evaluation. We describe the general architecture of the prototype, including the graphical component, and focus on its main feature: the incorporation, in the way the dialogue flows, of Behavior Change Techniques, identified through a theoretical framework, the Behaviour Change Wheel. Usability was experimentally evaluated in field tests in a purposive sample of 20 participants (11 older adults with T2D and 9 experts). The Portuguese version of the System Usability Scale was employed, supplemented with qualitative data from open questions, diaries, digital notes and telephone follow-ups. The aggregated mean SUS score was 73,75 (SD 13,31), which corresponds to a borderline rating of excellent. Textual data were content analyzed and will be prioritized to further improve usability.

Screening diabetes mellitus 2 based on electronic health records using temporal features

The prevalence of type 2 diabetes mellitus is increasing worldwide. Current methods of treating diabetes remain inadequate, and therefore, prevention with screening methods is the most appropriate process to reduce the burden of diabetes and its complications. We propose a new prognostic approach for type 2 diabetes mellitus based on electronic health records without using the current invasive techniques that are related to the disease (e.g. glucose level or glycated hemoglobin (HbA1c)). Our methodology is based on machine learning frameworks with data enrichment using temporal features. As as result our predictive model achieved an area under the receiver operating characteristics curve with a random forest classifier of 84.22 percent when including data information from 2009 to 2011 to predict diabetic patients in 2012, 83.19 percent when including temporal features, and 83.72 percent after applying temporal features and feature selection. We conclude that he pathology prediction is possible and efficient using the patient's progression information over the years and without using the invasive techniques that are currently used for type 2 diabetes mellitus classification.

Discovery and Clinical Decision Support for Personalized Healthcare

With the advent of electronic health records, more data are continuously collected for individual patients, and more data are available for review from past patients. Despite this, it has not yet been possible to successfully use this data to systematically build clinical decision support systems that can produce personalized clinical recommendations to assist clinicians in providing individualized healthcare. In this paper, we present a novel approach, discovery engine (DE), that discovers which patient characteristics are most relevant for predicting the correct diagnosis and/or recommending the best treatment regimen for each patient. We demonstrate the performance of DE in two clinical settings: diagnosis of breast cancer as well as a personalized recommendation for a specific chemotherapy regimen for breast cancer patients. For each distinct clinical recommendation, different patient features are relevant; DE can discover these different relevant features and use them to recommend personalized clinical decisions. The DE approach achieves a 16.6% improvement over existing state-of-the-art recommendation algorithms regarding kappa coefficients for recommending the personalized chemotherapy regimens. For diagnostic predictions, the DE approach achieves a 2.18% and 4.20% improvement over existing state-of-the-art prediction algorithms regarding prediction error rate and false positive rate, respectively. We also demonstrate that the performance of our approach is robust against missing information and that the relevant features discovered by DE are confirmed by clinical references.

Mobile Applications for Type 2 Diabetes Risk Estimation: a Systematic Review

Screening for chronical diseases like type 2 diabetes can be done using different methods and various risk tests. This study present a review of type 2 diabetes risk estimation mobile applications focusing on their functionality and availability of information on the underlying risk calculators. Only 9 out of 31 reviewed mobile applications, featured in three major mobile application stores, disclosed the name of risk calculator used for assessing the risk of type 2 diabetes. Even more concerning, none of the reviewed applications mentioned that they are collecting the data from users to improve the performance of their risk estimation calculators or offer users the descriptive statistics of the results from users that already used the application. For that purpose the questionnaires used for calculation of risk should be upgraded by including the information on the most recent blood sugar level measurements from users. Although mobile applications represent a great future potential for health applications, developers still do not put enough emphasis on informing the user of the underlying methods used to estimate the risk for a specific clinical condition.