Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes.

Patients with type 1 diabetes must continually decide how much insulin to inject before each meal to maintain blood glucose levels within a healthy range. Recent research has worked on a solution for this burden, showing the potential of reinforcement learning as an emerging approach for the task of controlling blood glucose levels. In this paper, we test and evaluate several deep Q-learning algorithms for automated and personalized blood glucose regulation in an in silico type 1 diabetes patient with the goal of estimating and delivering proper insulin doses. The proposed algorithms are model-free approaches with no prior information about the patient. We used the Hovorka model with meal variation and carbohydrate counting errors to simulate the patient included in this work. Our experiments compare different deep Q-learning extensions showing promising results controlling blood glucose levels, with some of the proposed algorithms outperforming standard baseline treatment.

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators.

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention's generalizability and interoperability with existing systems, as well as the inner settings' data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

Reinforcement learning application in diabetes blood glucose control: A systematic review.

BACKGROUND: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient's own data. OBJECTIVE: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support and personalized feedback in the context of DM. METHODS: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection. RESULTS: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. CONCLUSIONS: The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms.

Clinically relevant peach allergy is related to peach lipid transfer protein, Pru p 3, in the Spanish population.

BACKGROUND: Sensitization to peach and related Rosaceae fruits without clinical expression is commonly observed as the result of the extensive cross-reactivity of IgE antibodies directed toward lipid transfer proteins (LTPs), Bet v 1 homologues, profilins, and carbohydrate determinants. OBJECTIVE: We aimed to study whether there are any clinical or immunologic differences between patients allergic to peach and those who have a current clinically irrelevant sensitization to this fruit. METHODS: One hundred subjects with adverse reactions to peach were evaluated by medical history, skin prick tests with fresh peach and purified peach LTP (Pru p 3), and specific IgE determinations to peach, rBet v 1, and rBet v 2 (birch profilin). Clinical reactivity to peach was established by double-blind, placebo-controlled food challenges. The clinical characteristics and the in vivo and in vitro tests were compared between allergic and nonallergic patients. RESULTS: Peach allergy was confirmed in 76 patients and ruled out in 16; 2 patients dropped out, and the study was not conclusive in 6 individuals (placebo reactors). Pollen allergy was found in 76% of the allergic patients and in 100% of the nonallergic patients. Positive responses to Pru p 3, rBet v 1, and rBet v 2 were observed in 62%, 7%, and 34% of patients allergic to peach, respectively. The sensitization rate to Pru p 3 was significantly higher among subjects allergic than nonallergic to peach (62% vs 31%, P =.02). IgE responses to rBet v 2 were more frequent among subjects allergic to pollen, but no difference was observed in the presence or absence of peach allergy. CONCLUSIONS: Pru p 3 is the major allergen of peach in our population, and the IgE response to this allergen is related to the clinical expression of peach allergy. Sensitization to profilin is observed in those patients with an associated pollen allergy but does not appear to be related to the clinical reactivity to peach.