Matthew T. Wiley

Characterizing Blood Glucose Variability Using New Metrics with Continuous Glucose Monitoring Data

Objective: Glycemic variability contributes to oxidative stress, which has been linked to the pathogenesis of the long-term complications of diabetes. Currently, the best metric for assessing glycemic variability is mean amplitude of glycemic excursion (MAGE); however, MAGE is not in routine clinical use. A glycemic variability metric in routine clinical use could potentially be an important measure of overall glucose control and a predictor of diabetes complication risk not detected by glycosylated hemoglobin (A1C) levels. This study aimed to develop and evaluate new automated metrics of glycemic variability that could be routinely applied to continuous glucose monitoring (CGM) data to assess and enhance glucose control. Method: Individual 24 h CGM tracings from our clinical diabetes research database were scored for MAGE and two additional metrics designed to compensate for aspects of variability not captured by MAGE: (1) number of daily glucose fluctuations >75 mg/dl that leave the normal range (70–175 mg/dl), or excursion frequency, and (2) total daily fluctuation, or distance traveled. These scores were used to train machine learning algorithms to recognize excessive variability based on physician ratings of daily CGM charts, producing a third metric of glycemic variability: perceived variability. Finger stick A1C (average) and serum 1,5-anhydroglucitol (postprandial) levels were used as clinical markers of overall glucose control for comparison. Results: Mean amplitude of glycemic excursion, excursion frequency, and distance traveled did not adequately quantify the glycemic variability visualized by physicians who evaluated the daily CGM plots. A naive Bayes classifier was developed that characterizes CGM tracings based on physician interpretations of tracings. Preliminary results suggest that the number of excessively variable days, as determined by this naive Bayes classifier, may be an effective way to automatically assess glycemic variability of CGM data. This metric more closely reflects 90-day changes in serum 1,5-anhydroglucitol levels than does MAGE. Conclusion: We have developed a new automated metric to assess overall glycemic variability in people with diabetes using CGM, which could easily be incorporated into commercially available CGM software. Additional work to validate and refine this metric is underway. Future studies are planned to correlate the metric with both urinary 8-iso-prostaglandin F2 alpha excretion and serum 1,5-anhydroglucitol levels to see how well it identifies patients with high glycemic variability and increased markers of oxidative stress to assess risk for long-term complications of diabetes.

Pharmaceutical drugs chatter on Online Social Networks

The ubiquity of Online Social Networks (OSNs) is creating new sources for healthcare information, particularly in the context of pharmaceutical drugs. We aimed to examine the impact of a given OSNs characteristics on the content of pharmaceutical drug discussions from that OSN. We compared the effect of four distinguishing characteristics from ten different OSNs on the content of their pharmaceutical drug discussions: (1) General versus Health OSN; (2) OSN moderation; (3) OSN registration requirements; and (4) OSNs with a question and answer format. The effects of these characteristics were measured both quantitatively and qualitatively. Our results show that an OSNs characteristics indeed affect the content of its discussions. Based on their information needs, healthcare providers may use our findings to pick the right OSNs or to advise patients regarding their needs. Our results may also guide the creation of new and more effective domain-specific health OSNs. Further, future researchers of online healthcare content in OSNs may find our results informative while choosing OSNs as data sources. We reported several findings about the impact of OSN characteristics on the content of pharmaceutical drug discussion, and synthesized these findings into actionable items for both healthcare providers and future researchers of healthcare discussions on OSNs. Future research on the impact of OSN characteristics could include user demographics, quality and safety of information, and efficacy of OSN usage.

Emerging Applications for Intelligent Diabetes Management

Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task and shares difficulties encountered in transitioning AI technology from university researchers to patients and physicians.

The 4 diabetes support system: a case study in CBR research and development

This paper presents the 4 Diabetes Support SystemTM (4DSS) project as a case study in case-based reasoning (CBR) research and development. This project aims to help patients with type 1 diabetes on insulin pump therapy achieve and maintain good blood glucose control. Over the course of seven years and three clinical research studies, a series of defining cases altered the research and development path. Each of these cases suggested a new, unanticipated research direction or clinical application. New AI technologies, including naive Bayes classification and support vector regression, were incorporated. New medical research into glycemic variability and blood glucose prediction was undertaken. The CBR research paradigm has provided a strong framework for medical research as well as for artificial intelligence (AI) research. This new work has the potential to positively impact the health and wellbeing of patients with diabetes. This paper shares the 4DSS project experience.

Automatic Detection of Excessive Glycemic Variability for Diabetes Management

Glycemic variability, or fluctuation in blood glucose levels, is a significant factor in diabetes management. Excessive glycemic variability contributes to oxidative stress, which has been linked to the development of long-term diabetic complications. An automated screen for excessive glycemic variability, based on the readings from continuous glucose monitoring (CGM) systems, would enable early identification of at risk patients. In this paper, we present an automatic approach for learning variability models that can routinely detect excessive glycemic variability when applied to CGM data. Naive Bayes (NB), Multilayer Perceptron (MP), and Support Vector Machine (SVM) models are trained and evaluated on a dataset of CGM plots that have been manually annotated with respect to glycemic variability by two diabetes experts. In order to alleviate the impact of noise, the CGM plots are smoothed using cubic splines. Automatic feature selection is then performed on a rich set of pattern recognition features. Empirical evaluation shows that the top performing model obtains a state of the art accuracy of 93.8%, substantially outperforming a previous NB model.

IRanker: Query-Specific Ranking of Reviewed Items

Item (e.g., product) reviews are one of the most popular types of user-generated content in Web 2.0. Reviews have been effectively used in collaborative filtering to recommend products to users based on similar users, and also to compute a products star rating. However, little work has studied how reviews can be used to perform query-specific ranking of items. In this paper, we present efficient top-k algorithms to rank items, by weighing each reviews rating by its relevance to the user query. We propose a non-random access algorithm and perform a comprehensive evaluation of our method on multiple datasets. We show that our solution significantly outperforms the baseline approach in terms of query response time.