Background:The Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system provides personalised bolus advice for people with Type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalised carbohydrate recommendations and dynamic bolus insulin constraint. We evaluated the safety and feasibility of the PEPPER system compared to a standard bolus calculator. Methods:This was an open-labelled multicentre randomized controlled cross-over study. Following 4week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12-weeks. Participants then crossed over after a wash-out period. The primary end-point was percentage time in range (TIR, 3.9mmol/L-10.0mmol/L (70-180mg/dL)). Secondary outcomes included glycaemic variability, quality of life, and outcomes on the safety system and insulin recommender.Results: 54 participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 (52.1-67.8) % vs 58.4 (49.6-64.3) % respectively, p=0.27). For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Conclusions:The PEPPER system was safe but did not change glycaemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness.
Type 1 Diabetes Mellitus (T1DM) is a chronic disease. Those who have it must administer themselves with insulin to control their blood glucose level. It is difficult to estimate the correct insulin dosage due to the complex glucose metabolism, which can lead to less than optimal blood glucose levels. This paper presents PepperRec, a case-based reasoning (CBR) bolus insulin recommender system capable of dealing with an unrestricted number of situations in which T1DM persons can find themselves. PepperRec considers several factors that affect glucose metabolism, such as data about the physical activity of the user, and can also cope with missing values for these factors. Based on CBR methodology, PepperRec uses new methods to adapt past recommendations to the current state of the user, and retains updated historical patient information to deal with slow and gradual changes in the patient over time (concept drift). The proposed approach is tested using the UVA/PADOVA simulator with 33 virtual subjects and compared with other methods in the literature, and with the default insulin therapy of the simulator. The achieved results demonstrate that PepperRec increases the amount of time the users are in their target glycaemic range, reduces the time spent below it, while maintaining, or even reducing, the time spent above it.
Abstract. Recommender systems help users to identify particular items that best match their tastes or preferences. When we apply the agent theory to this domain, a standard centralized recommender system becomes a distributed world of recommender agents. Therefore, due to the agent's world, a new information filtering method appears: the opinion-based filtering method. Its main idea is to consider other agents as personal entities which you can rely on or not. Recommender agents can ask their reliable friends for an opinion about a particular item and filter large sets of items based on it. Reliability is expressed through a trust value with which each agent labels its neighbors. Thus, the opinion-based filtering method needs a model of trust in the collaborative world. The model proposed emphasizes proactiveness since the agent looks for other agents in a situation of lack of information instead of remaining passive or providing either a negative or empty answer to the user. Finally, our social model of trust exploits interactiveness while preserving privacy.
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