Enhancing Context Specifications for Dependable Adaptive Systems: A Data Mining Approach

Context: Adaptive systems are expected to cater for various operational contexts by having multiple strategies in achieving their objectives and the logic for matching strategies to an actual context. The prediction of relevant contexts at design time is paramount for dependability. With the current trend on using data mining to support the requirements engineering process, this task of understanding context for adaptive system at design time can benefit from such techniques as well. Objective: The objective is to provide a method to refine the specification of contextual variables and their relation to strategies for dependability. This refinement shall detect dependencies between such variables, priorities in monitoring them, and decide on their relevance in choosing the right strategy in a decision tree. Method: Our requirements-driven approach adopts the contextual goal modelling structure in addition to the operationalization values of sensed information to map contexts to the system’s behaviour. We propose a design time analysis process using a subset of data mining algorithms to extract a list of relevant contexts and their related variables, tasks, and/or goals. Results: We experimentally evaluated our proposal on a Body Sensor Network system (BSN), simulating 12 resources that could lead to a variability space of 4096 possible context conditions. Our approach was able to elicit subtle contexts that would significantly affect the service provided to assisted patients and relations between contexts, assisting the decision on their need, and priority in monitoring. Conclusion: The use of some data mining techniques can mitigate the lack of precise definition of contexts and their relation to system strategies for dependability. Our method is practical and supportive to traditional requirements specification methods, which typically require intense human intervention

Machine Learning for Physiological Time Series: Representing and Controlling Blood Glucose for Diabetes Management

Type 1 diabetes is a chronic health condition affecting over one million patients in the US, where blood glucose (sugar) levels are not well regulated by the body. Researchers have sought to use physiological data (e.g., blood glucose measurements) collected from wearable devices to manage this disease, either by forecasting future blood glucose levels for predictive alarms, or by automating insulin delivery for blood glucose management. However, the application of machine learning (ML) to these data is hampered by latent context, limited supervision and complex temporal dependencies. To address these challenges, we develop and evaluate novel ML approaches in the context of i) representing physiological time series, particularly for forecasting blood glucose values and ii) decision making for when and how much insulin to deliver. When learning representations, we leverage the structure of the physiological sequence as an implicit information stream. In particular, we a) incorporate latent context when predicting adverse events by jointly modeling patterns in the data and the context those patterns occurred under, b) propose novel types of self-supervision to handle limited data and c) propose deep models that predict functions underlying trajectories to encode temporal dependencies. In the context of decision making, we use reinforcement learning (RL) for blood glucose management. Through the use of an FDA-approved simulator of the glucoregulatory system, we achieve strong performance using deep RL with and without human intervention. However, the success of RL typically depends on realistic simulators or experimental real-world deployment, neither of which are currently practical for problems in health. Thus, we propose techniques for leveraging imperfect simulators and observational data. Beyond diabetes, representing and managing physiological signals is an important problem. By adapting techniques to better leverage the structure inherent in the data we can help overcome these challenges.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163134/1/ifox_1.pd

Machine Learning of Lifestyle Data for Diabetes

Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The output of data analysis, as potentially valuable patterns or knowledge, could be the incentives for users to contribute more data. We believe that the incorporation of machine learning technologies in mobile diabetes management could tackle these challenge simultaneously. In this thesis, we propose, build, and evaluate an intelligent mobile diabetes management system, called GlucoGuide for T2D patients. GlucoGuide conveniently aggregates varieties of lifestyle data collected via mobile devices, analyzes the data with machine learning models, and outputs recommendations. The most complicated part of SMBG is diet management. GlucoGuide aims to address this crucial issue using classification models and camera-based automatic data logging. The proposed model classifies each food item into three recommendation classes using its nutrient and textual features. Empirical studies show that the food classification task is effective. A lifestyle-data-driven recommendations framework in GlucoGuide can output short-term and personalized recommendations of lifestyle changes to help patients stabilize their blood glucose level. To evaluate performance and clinical effectiveness of this framework, we conduct a three-month clinical trial on human subjects, in collaboration with Dr. Petrella (MD). Due to the high cost and complexity of trials on humans, a small but representative subject group is involved. Two standard laboratory blood tests for diabetes are used before and after the trial. The results are quite remarkable. Generally speaking, GlucoGuide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months, depending on their before-trial diabetic conditions. cThis clinical dataset has also been expanded and enhanced to generate scientifically controlled artificial datasets. Such datasets can be used for varieties of machine learning empirical studies, as our on-going and future research works. GlucoGuide now is a university spin-off, allowing us to collect a large scale of practical diabetic lifestyle data and make potential impact on diabetes treatment and management

Blood glucose prediction for type 1 diabetes using generative adversarial networks

Maintaining blood glucose in a target range is essential for people living with Type 1 diabetes in order to avoid excessive periods in hypoglycemia and hyperglycemia which can result in severe complications. Accurate blood glucose prediction can reduce this risk and enhance early interventions to improve diabetes management. However, due to the complex nature of glucose metabolism and the various lifestyle related factors which can disrupt this, diabetes management still remains challenging. In this work we propose a novel deep learning model to predict future BG levels based on the historical continuous glucose monitoring measurements, meal ingestion, and insulin delivery. We adopt a modified architecture of the generative adversarial network that comprises of a generator and a discriminator. The generator computes the BG predictions by a recurrent neural network with gated recurrent units, and the auxiliary discriminator employs a one-dimensional convolutional neural network to distinguish between the predictive and real BG values. Two modules are trained in an adversarial process with a combination of loss. The experiments were conducted using the OhioT1DM dataset that contains the data of six T1D contributors over 40 days. The proposed algorithm achieves an average root mean square error (RMSE) of 18.34 ± 0.17 mg/dL with a mean absolute error (MAE) of 13.37 ± 0.18 mg/dL for the 30-minute prediction horizon (PH) and an average RMSE of 32.31 ± 0.46 mg/dL with a MAE of 24.20 ± 0.42 for the 60-minute PH. The results are compared for clinical relevance using the Clarke error grid which confirms the promising performance of the proposed model

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

Globally, the aging and the lifestyle lead to rabidly increment of the number of type two diabetes (T2D) patients. Critically, T2D considers as one of the most challenging healthcare issue. Importantly, physical activity (PA) plays a vital role of improving glycemic control T2D. However, daily monitoring of T2D using wearable devices/ sensors have a crucial role to monitor glucose levels in the blood. Nowadays, daily physical activity (PA) and exercises have been used to manage T2D. The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors. Finally, challenges and future trends are also highlighted

Association between Physical Activity and Sport Participation on Hemoglobin A1c among Children and Adolescents with Type 1 Diabetes

Purpose: To determine associations between physical activity (PA) and sport participation on HbA1c levels in children with type 1 diabetes (T1D). Method: Pediatric patients with T1D were invited to complete a PA and sport participation survey. Data were linked to their medical records for demographic characteristics, diabetes treatment and monitoring plans, and HbA1c levels. Results: Participants consisted of 71 females and 81 males, were 13 +- 3 years old with an average HbA1c level of 8.75 +- 1.81. Children accumulating 60 min of activity 3 days or more a week had significantly lower HbA1c compared to those who accumulated less than 3 days (p \u3c 0.01) of 60 min of activity. However, there was no significant difference in HbA1c values based on sport participation groups. A multiple linear regression model indicated that PA, race, age, duration of diagnosis, and CGM use all significantly predicted HbA1c (p \u3c 0.05). Conclusion: This study demonstrated the significant relationship between daily PA and HbA1c. Those in this sample presented with lower HbA1c values even if accumulating less than the recommended number of days of activity. Further, it was shown that sport participation alone may not be adequate enough to impact HbA1c in a similar manner

Challenges in biomedical data science: data-driven solutions to clinical questions

Data are influencing every aspect of our lives, from our work activities, to our spare time and even to our health. In this regard, medical diagnosis and treatments are often supported by quantitative measures and observations, such as laboratory tests, medical imaging or genetic analysis. In medicine, as well as in several other scientific domains, the amount of data involved in each decision-making process has become overwhelming. The complexity of the phenomena under investigation and the scale of modern data collections has long superseded human analysis and insights potential

Identifying Clinical Phenotypes of Type 1 Diabetes for the Co-Optimization of Weight and Glycemic Control

Obesity is an increasing concern in the clinical care of youth with type 1 diabetes (T1D). Standard approaches to co-optimize weight and glycemic control are challenged by profound population-level heterogeneity. Therefore, the goal of the dissertation was to apply novel analytic methods to understand heterogeneity in the co-occurrence of weight, glycemia, and underlying patterns of minute-to-minute dysglycemia among youth with T1D. Data from the SEARCH for Diabetes in Youth study were used to characterize subgroups of youth with T1D showing similar weight status and level of glycemic control as distinct ‘weight-glycemia phenotypes’ of T1D. Cross-sectional weight-glycemia phenotypes were identified at the 5+ year follow-up visit (n=1,817) using hierarchical clustering on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c), generated by reinforcement learning tree predictions. Longitudinal weight-glycemia phenotypes spanning eight years were identified with longitudinal k-means clustering using baseline and follow-up BMIz and HbA1c measures (n=570). Logistic regression modeling tested for differences in the emergence of early/subclinical diabetes complications across subgroups. Seven-day blinded continuous glucose monitoring (CGM) data from baseline of the Flexible Lifestyles Empowering Change randomized trial (n=234, 13-16 years, HbA1c 8-13%) was clustered with a neural network approach to identify subgroups of adolescents with T1D and elevated HbA1c sharing patterns in their CGM data as ‘dysglycemia phenotypes.’ We identified six cross-sectional weight-glycemia phenotypes, including four normal-weight, one overweight, and one subgroup with obesity. Subgroups showed striking differences in other sociodemographic and clinical characteristics suggesting underlying health inequity. We identified four longitudinal weight-glycemia phenotypes associated with different patterns of early/subclinical complications, providing evidence that exposure to co-occurring obesity and worsening glycemic control may accelerate the development and increase the burden of co-morbid complications. We identified three dysglycemia phenotypes with significantly different patterns in hypoglycemia, hyperglycemia, glycemic variability, and 18-month changes in HbA1c. Patient-level drivers of the dysglycemia phenotypes appear to be different from risk factors for poor glycemic control as measured by HbA1c. These studies provide pragmatic, clinically-relevant examples of how novel statistics may be applied to data from T1D to derive patient subgroups for tailored interventions to improve weight alongside glycemic control.Doctor of Philosoph