Optimal Regulation of Blood Glucose Level in Type I Diabetes using Insulin and Glucagon

The Glucose-Insulin-Glucagon nonlinear model [1-4] accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.Comment: Accepted for publication in PLOS ON

Adiponectin improves coronary no-reflow injury by protecting the endothelium in rats with type 2 diabetes mellitus.

To determine the effect of adiponectin (APN) on the coronary no-reflow (NR) injury in rats with Type 2 diabetes mellitus (T2DM), 80 male Sprague-Dawley rats were fed with a high-sugar-high-fat diet to build a T2DM model. Rats received vehicle or APN in the last week and then were subjected to myocardial ischemia reperfusion (MI/R) injury. Endothelium-dependent vasorelaxation of the thoracic aorta was significantly decreased and serum levels of endothelin-1 (ET-1), intercellular cell adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) were noticably increased in T2DM rats compared with rats without T2DM. Serum APN was positively correlated with the endothelium-dependent vasorelaxation, but negatively correlated with the serum level of ET-1. Treatment with APN improved T2DM-induced endothelium-dependent vasorelaxation, recovered cardiac function, and decreased both NR size and the levels of ET-1, ICAM-1 and VCAM-1. Hypoadiponectinemia was associated with the aggravation of coronary NR in T2DM rats. APN could alleviate coronary NR injury in T2DM rats by protecting the endothelium and improving microcirculation

A Smartphone-Based Tool for Rapid, Portable, and Automated Wide-Field Retinal Imaging.

Purpose:High-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality and restricted field of view can limit their utility. We developed and clinically tested a smartphone-based system that addresses these challenges with automation-assisted imaging. Methods:The system was designed to improve smartphone retinal imaging by combining automated fixation guidance, photomontage, and multicolored illumination with optimized optics, user-tested ergonomics, and touch-screen interface. System performance was evaluated from images of ophthalmic patients taken by nonophthalmic personnel. Two masked ophthalmologists evaluated images for abnormalities and disease severity. Results:The system automatically generated 100° retinal photomontages from five overlapping images in under 1 minute at full resolution (52.3 pixels per retinal degree) fully on-phone, revealing numerous retinal abnormalities. Feasibility of the system for diabetic retinopathy (DR) screening using the retinal photomontages was performed in 71 diabetics by masked graders. DR grade matched perfectly with dilated clinical examination in 55.1% of eyes and within 1 severity level for 85.2% of eyes. For referral-warranted DR, average sensitivity was 93.3% and specificity 56.8%. Conclusions:Automation-assisted imaging produced high-quality, wide-field retinal images that demonstrate the potential of smartphone-based retinal cameras to be used for retinal disease screening. Translational Relevance:Enhancement of smartphone-based retinal imaging through automation and software intelligence holds great promise for increasing the accessibility of retinal screening

Deep learning for electronic health records: risk prediction, explainability, and uncertainty

Background: Risk models are essential for care planning and disease prevention. The unsatisfactory performance of the established clinical models has raised broad awareness and concerns. An accurate, explainable, and reliable risk model is highly beneficial but remains a challenge. Objective: This thesis aims to develop deep learning models that can make more accurate risk predictions with the provision of uncertainty estimation and the ability to provide medical explanations using a large and representative electronic health records (EHR) dataset. Methods: We investigated three directions in this thesis: risk prediction, explainability, and uncertainty estimation. For risk prediction, we investigated deep learning tools that can incorporate the minimal processed EHR for modelling and comprehensively compared them with the established machine learning and clinical models. Additionally, the post-hoc explanations were applied to deep learning models for medical information retrieval, and we specifically looked into explanations in risk association and counterfactual reasoning. Uncertainty estimation was qualitatively investigated using probabilistic modelling techniques. Our analyses relied on Clinical Practice Research Datalink, which contains anonymised EHR collected from primary care, secondary care, and death registration and is representative of the UK population. Results: We introduced a deep learning model, named BEHRT, that can incorporate minimal processed EHR for risk prediction. Without expert engagement, it learned meaningful representations that can automatically cluster highly correlated diseases. Compared to the established machine learning and clinical models that relied on expert- selected predictors, our proposed deep learning model showed superior performance on a wide range of risk prediction tasks and highlighted the necessity of recalibration when applying a risk model to a population with severe prior distribution shifts, and the importance of regular model updating to preserve the model’s discrimination performance under temporal data shifts. Additionally, we showed that the deep learning model explanation is an excellent tool for discovering risk factors. By explaining the deep learning model, we not only identified factors that were highly consistent with the established evidence but also those that have not been considered in expert-driven studies. Furthermore, the deep learning model also captured the interplay between risk and treated risk and the differential association of medications across different years, which would be difficult if the temporal context was not included in the modelling. Besides the explanations in terms of association, we introduced a framework that can achieve accurate risk prediction, while enabling counterfactual reasoning under hypothetical interventions. This offers counterfactual explanations that could inform clinicians for selection of those who will benefit the most. We demonstrated the benefit of the proposed framework using two exemplary case studies. Furthermore, transforming a deterministic deep learning model to probabilistic can make predictions with an uncertainty range. We showed that such information has many potential implications in practice, such as quantifying the confidence of a decision, indicating data insufficiency, distinguishing the correct and incorrect predictions, and indicating risk associations. Conclusions: Deep learning models led to substantially improved performance for risk prediction. The ability of uncertainty estimation can quantify the confidence of risk prediction to further inform clinical decision-making. Deep learning model explanation can generate hypotheses to guide medical research and provide counterfactual analysis to assist clinical decision-making. This encouraging evidence supports the great potential of incorporating deep learning methods into electronic health records to inform a wide range of health applications such as care planning, disease prevention, and medical study design

The role of GP’s compensation schemes in diabetes care: evidence from panel data

The design of incentive schemes that improve quality of care is a central issue for the healthcare sector. Nowadays we observe many pay-for-performance programs, where payment is contingent on meeting indicators of provider effort, but also other alternative strategies have been introduced, for example programs rewarding physicians for participation in diseases management plans. Although it has been recognised that incentive-based remuneration schemes can have an impact on GP behaviour, there is still weak empirical evidence on the extent to which such programs influence health outcomes. We investigate the impact of financial incentives in Regional and Local Health Authority contracts for primary care in the Italian Region Emilia Romagna for the years 2003-05. We focus on avoidable hospitalisations (Ambulatory Care Sensitive Conditions) for patients affected by type 2 diabetes mellitus, for which the assumption of responsibility and the adoption of clinical guidelines are specifically rewarded. We estimate a panel count data model using a Negative Binomial distribution to test the hypothesis that, other things equal, patients under the responsibility of GPs receiving a higher share of their income through these programs are less likely to experience avoidable hospitalisations. Our findings support the hypothesis that financial transfers may contribute to improve quality of care, even when they are not based on the ex-post verification of performances.

Proceeding: 3rd Java International Nursing Conference 2015 “Harmony of Caring and Healing Inquiry for Holistic Nursing Practice; Enhancing Quality of Care”, Semarang, 20-21 August 2015

This is the proceeding of the 3rd Java International Nursing Conference 2015 organized by School of Nursing, Faculty of Medicine, Diponegoro University, in collaboration with STIKES Kendal. The conference was held on 20-21 August 2015 in Semarang, Indonesia. The conference aims to enable educators, students, practitioners and researchers from nursing, medicine, midwifery and other health sciences to disseminate and discuss evidence of nursing education, research, and practices to improve the quality of care. This conference also provides participants opportunities to develop their professional networks, learn from other colleagues and meet leading personalities in nursing and health sciences. The 3rd JINC 2015 was comprised of keynote lectures and concurrent submitted oral presentations and poster sessions. The following themes have been chosen to be the focus of the conference: (a) Multicenter Science: Physiology, Biology, Chemistry, etc. in Holistic Nursing Practice, (b) Complementary Therapy in Nursing and Complementary, Alternative Medicine: Alternative Medicine (Herbal Medicine), Complementary Therapy (Cupping, Acupuncture, Yoga, Aromatherapy, Music Therapy, etc.), (c) Application of Inter-professional Collaboration and Education: Education Development in Holistic Nursing, Competencies of Holistic Nursing, Learning Methods and Assessments, and (d) Application of Holistic Nursing: Leadership & Management, Entrepreneurship in Holistic Nursing, Application of Holistic Nursing in Clinical and Community Settings