Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.This work is funded by the EPSRC and China Market Association

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.EThOS - Electronic Theses Online ServiceEPSRCChina Market AssociationGBUnited Kingdo

COHORT IDENTIFICATION FROM FREE-TEXT CLINICAL NOTES USING SNOMED CT’S SEMANTIC RELATIONS

In this paper, a new cohort identification framework that exploits the semantic hierarchy of SNOMED CT is proposed to overcome the limitations of supervised machine learning-based approaches. Eligibility criteria descriptions and free-text clinical notes from the 2018 National NLP Clinical Challenge (n2c2) were processed to map to relevant SNOMED CT concepts and to measure semantic similarity between the eligibility criteria and patients. The eligibility of a patient was determined if the patient had a similarity score higher than a threshold cut-off value, which was established where the best F1 score could be achieved. The performance of the proposed system was evaluated for three eligibility criteria. The current framework’s macro-average F1 score across three eligibility criteria was higher than the previously reported results of the 2018 n2c2 (0.933 vs. 0.889). This study demonstrated that SNOMED CT alone can be leveraged for cohort identification tasks without referring to external textual sources for training.Doctor of Philosoph

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

Painful diabetic neuropathy: Exploring management options

Painful diabetic neuropathy (PDN) is one microvascular complication of diabetes mellitus (DM) and the focus of this thesis. PDN is a neuropathic pain condition characterised by severe burning pain in the feet and sometimes hands. It has significant impacts on peoples’ mobility, sleep quality and overall quality of life. The personal and societal burden associated with DM and PDN is predicated to rise as prevalence rates increase.Pharmacological management of PDN is often less than optimal, and people are left with few strategies to cope. Multidisciplinary pain management programmes (PMPs) use physical activity and psychological coping strategies to help people live better with persistent pain, yet people with PDN are rarely referred. It is unknown whether these strategies would be appropriate to help people live with PDN. This thesis aimed to: 1) locate and appraise all literature relating to physical activity and psychological coping strategies in PDN; 2) interview people with PDN and explore how PDN impacted on their lives; 3) explore the perspectives of patients and clinicians on the relevance of PMP approaches; and 4) explore patients’ treatment priorities and whether these might be addressed by PMP strategies.To address these aims, firstly a systematic literature review was conducted. The review identified a paucity of studies investigating physical activity or psychological coping strategies for PDN. Two interview studies were conducted, and data were analysed using thematic analysis (TA). A study with patients (n=23) found the impacts of PDN were wide ranging, people had experimented with many coping strategies unsuccessfully and there was some scepticism that PMP strategies were relevant to PDN, though few participants had direct experience of them. The second study interviewed specialist diabetes and pain clinicians and representatives from primary care (n=19). Clinicians relied primarily on medication strategies and did not have alternatives when these failed. Diabetes clinicians highlighted that people with PDN were medically complex patients and were at risk of tissue damage from too much physical activity. Pain clinicians felt PMP strategies could be adapted to suit the population with PDN.Informed by the patient interview study, an Internet survey was developed to explore the management priorities of people with PDN (n=63 respondents). Sleep disturbance was the top priority in all subgroups analysed. There were six impacts most frequently prioritised by respondents, which did not include pain. Potential clinical management strategies for these impacts have been described, and suggestions made for future research. This thesis has shown a scarcity of existing evidence for non-pharmacological strategies in the management of PDN. PMP strategies were not necessarily viewed as appropriate by patient participants. The impacts prioritized by people with PDN could however be matched to management strategies from other conditions where persistent pain is common. There is no a priori reason why these strategies could not be trialled with PDN. Managing the impacts of PDN on peoples’ lives remains a complex process

The relationships between mindfulness, diabetes-related distress, selected demographic variables, and self-management in adults with Type 2 diabetes

Type 2 diabetes is a disease of worldwide scope and epidemic proportion. Two hundred and eighty-five million individuals have been diagnosed worldwide--a number expected to rise to 330 million by 2025 (Unwin, Whiting, &amp; Roglic, 2010) and to 366 million by 2030 (Adriaanse et al., 2008). It is estimated that 18.8 million diagnosed and 7.0 million undiagnosed Americans have type 2 diabetes, numbers expected to rise to a total of 48.3 million by 2050 (Centers for Disease Control [CDC], 2011; Geiss &amp; Cowie, 2011; Narayan, Williams, Gregg, &amp; Cowie 2011). A recent American Diabetes Association (ADA) report estimated that the total costs of diabetes related health care rose from $174 billion in 2007 to$245 billion in 2012--figures that underscore the significant social costs associated with the disease (ADA, 2013). The considerable personal, social, and financial tolls of type 2 diabetes make effective self-management imperative. Diabetes-related distress (DRD) and mindfulness are two variables that are believed to significantly impact effective diabetes self-management yet more research is needed to better understand and empirically confirm these relationships. DRD is characterized by the negative emotional reactions to the diabetes diagnosis, threat of complications, self-management demands, and unsupportive interpersonal relationships (Polonsky et al., 1995, 2005; Gonzalez, Fisher, &amp; Polonsky, 2011). Recent studies indicate the relevance of mindfulness, the mindfulness components of awareness and acceptance, and the use of mindfulness-based interventions to enhance the self-management behaviors of individuals with type 2 diabetes (Gregg, Callaghan, Hayes, &amp; Glenn-Lawson, 2007; Hernandez, Bradish, Rodger, &amp; Rybansky, 1999; Ingadottir &amp; Halldorsdottir, 2008). However, to date the literature is incomplete in drawing an explicit connection between mindfulness, diabetes-related distress, and diabetes self-management. This study was designed to address this gap in the literature. The prevalence of type 2 diabetes, its related debilitating conditions (e.g., cardiovascular disease, vascular dementia, kidney disease, and diabetic retinopathy), and mental health implications, make the exploration of self-management pathways imperative so that counselors and counselor educators may develop a greater understanding of the type 2 diabetes condition and appropriate counseling approaches. Greater understanding of the mechanisms to better diabetes self-management, with mindfulness as the theoretical foundation, may pave the way for improved prevention and intervention efforts among health care and mental health professionals. The results of the current study indicated that mindfulness is a statistically significant predictor of self-management. Further, the results indicated social support as a significant predictor of self-management. The results suggest the potential value of the clinical application of mindfulness-based interventions with the type 2 diabetes population and continued development of resources that provide positive social support for the millions of people who are affected by this disease

STRUCTURAL AND LEXICAL METHODS FOR AUDITING BIOMEDICAL TERMINOLOGIES

Biomedical terminologies serve as knowledge sources for a wide variety of biomedical applications including information extraction and retrieval, data integration and management, and decision support. Quality issues of biomedical terminologies, if not addressed, could affect all downstream applications that use them as knowledge sources. Therefore, Terminology Quality Assurance (TQA) has become an integral part of the terminology management lifecycle. However, identification of potential quality issues is challenging due to the ever-growing size and complexity of biomedical terminologies. It is time-consuming and labor-intensive to manually audit them and hence, automated TQA methods are highly desirable. In this dissertation, systematic and scalable methods to audit biomedical terminologies utilizing their structural as well as lexical information are proposed. Two inference-based methods, two non-lattice-based methods and a deep learning-based method are developed to identify potentially missing hierarchical (or is-a) relations, erroneous is-a relations, and missing concepts in biomedical terminologies including the Gene Ontology (GO), the National Cancer Institute thesaurus (NCIt), and SNOMED CT. In the first inference-based method, the GO concept names are represented using set-of-words model and sequence-of-words model, respectively. Inconsistencies derived between hierarchical linked and unlinked concept pairs are leveraged to detect potentially missing or erroneous is-a relations. The set-of-words model detects a total of 5,359 potential inconsistencies in the 03/28/2017 release of GO and the sequence-of-words model detects 4,959. Domain experts’ evaluation shows that the set-of-words model achieves a precision of 53.78% (128 out of 238) and the sequence-of-words model achieves a precision of 57.55% (122 out of 212) in identifying inconsistencies. In the second inference-based method, a Subsumption-based Sub-term Inference Framework (SSIF) is developed by introducing a novel term-algebra on top of a sequence-based representation of GO concepts. The sequence-based representation utilizes the part of speech of concept names, sub-concepts (concept names appearing inside another concept name), and antonyms appearing in concept names. Three conditional rules (monotonicity, intersection, and sub-concept rules) are developed for backward subsumption inference. Applying SSIF to the 10/03/2018 release of GO suggests 1,938 potentially missing is-a relations. Domain experts’ evaluation of randomly selected 210 potentially missing is-a relations shows that SSIF achieves a precision of 60.61%, 60.49%, and 46.03% for the monotonicity, intersection, and sub-concept rules, respectively. In the first non-lattice-based method, lexical patterns of concepts in Non-Lattice Subgraphs (NLSs: graph fragments with a higher tendency to contain quality issues), are mined to detect potentially missing is-a relations and missing concepts in NCIt. Six lexical patterns: containment, union, intersection, union-intersection, inference-contradiction, and inference-union are leveraged. Each pattern indicates a potential specific type of error and suggests a potential type of remediation. This method identifies 809 NLSs exhibiting these patterns in the 16.12d version of NCIt, achieving a precision of 66% (33 out of 50). In the second non-lattice-based method, enriched lexical attributes from concept ancestors are leveraged to identify potentially missing is-a relations in NLSs. The lexical attributes of a concept are inherited in two ways: from ancestors within the NLS, and from all the ancestors. For a pair of concepts without a hierarchical relation, if the lexical attributes of one concept is a subset of that of the other, a potentially missing is-a relation between the two concepts is suggested. This method identifies a total of 1,022 potentially missing is-a relations in the 19.01d release of NCIt with a precision of 84.44% (76 out of 90) for inheriting lexical attributes from ancestors within the NLS and 89.02% (73 out of 82) for inheriting from all the ancestors. For the non-lattice-based methods, similar NLSs may contain similar quality issues, and thus exhaustive examination of NLSs would involve redundant work. A hybrid method is introduced to identify similar NLSs to avoid redundant analyses. Given an input NLS, a graph isomorphism algorithm is used to obtain its structurally identical NLSs. A similarity score between the input NLS and each of its structurally identical NLSs is computed based on semantic similarity between their corresponding concept names. To compute the similarity between concept names, the concept names are converted to vectors using the Doc2Vec document embedding model and then the cosine similarity of the two vectors is computed. All the structurally identical NLSs with a similarity score above 0.85 is considered to be similar to the input NLS. Applying this method to 10 different structures of NLSs in the 02/12/2018 release of GO reveals that 38.43% of these NLSs have at least one similar NLS. Finally, a deep learning-based method is explored to facilitate the suggestion of missing is-a relations in NCIt and SNOMED CT. Concept pairs exhibiting a containment pattern is the focus here. The problem is framed as a binary classification task, where given a pair of concepts, the deep learning model learns to predict whether the two concepts have an is-a relation or not. Positive training samples are existing is-a relations in the terminology exhibiting containment pattern. Negative training samples are concept-pairs without is-a relations that are also exhibiting containment pattern. A graph neural network model is constructed for this task and trained with subgraphs generated enclosing the pairs of concepts in the samples. To evaluate each model trained by the two terminologies, two evaluation sets are created considering newer releases of each terminology as a partial reference standard. The model trained on NCIt achieves a precision of 0.5, a recall of 0.75, and an F1 score of 0.6. The model trained on SNOMED CT achieves a precision of 0.51, a recall of 0.64 and an F1 score of 0.56

Strengthening field-based training in low and middle-income countries to build public health capacity: Lessons from Australia's Master of Applied Epidemiology program

BACKGROUND: The International Health Regulations (2005) and the emergence and global spread of infectious diseases have triggered a re-assessment of how rich countries should support capacity development for communicable disease control in low and medium income countries (LMIC). In LMIC, three types of public health training have been tried: the university-based model; streamed training for specialised workers; and field-based programs. The first has low rates of production and teaching may not always be based on the needs and priorities of the host country. The second model is efficient, but does not accord the workers sufficient status to enable them to impact on policy. The third has the most potential as a capacity development measure for LMIC, but in practice faces challenges which may limit its ability to promote capacity development. DISCUSSION: We describe Australia's first Master of Applied Epidemiology (MAE) model (established in 1991), which uses field-based training to strengthen the control of communicable diseases. A central attribute of this model is the way it partners and complements health department initiatives to enhance workforce skills, health system performance and the evidence-base for policies, programs and practice. SUMMARY: The MAE experience throws light on ways Australia could collaborate in regional capacity development initiatives. Key needs are a shared vision for a regional approach to integrate training with initiatives that strengthen service and research, and the pooling of human, financial and technical resources. We focus on communicable diseases, but our findings and recommendations are generalisable to other areas of public health

Cognitive Function, Self-care, and Glycemic Control in Rural Adults with Type 2 Diabetes

Cognitive Function, Self-Care, and Glycemic Control in Rural Adults with Type 2 Diabetes Abstract The prevalence of type 2 diabetes mellitus (DM) has increased dramatically over the past two decades, particularly among adults living in rural communities. Related health complications include structural brain changes and decreased cognitive function. Cognitive decline associated with DM may influence one’s ability to perform self-care and affect glycemic control. In turn, poor glycemic control contributes to increased complications associated with DM. Although one’s ability to maintain glycemic control may be highly dependent on cognitive abilities, there is limited understanding about the relationship between cognitive function, self-care, and glycemic control in rural adults with DM. Specific aims of this study were to: 1) examine the relationships between cognitive function, glycemic control, and contributing factors (age, years with DM, education category, cardiovascular (CV) risk, level of depression) in rural adults with DM; 2) examine whether cognitive function predicts glycemic control in rural adults with DM; 3) examine the relationship between cognitive function, self-care, and contributing factors (age, years with DM, education category, everyday problem-solving, and level of depression) in rural adults with DM; and, 4) examine whether cognitive function predicts self-care in rural adults with DM. This descriptive study included a convenience sample of (N=56) rural adults with DM. A face-to-face interview was conducted with each participant, where performance of the cognitive processes of attention, executive function, mental processing speed, and verbal episodic memory was measured with neuropsychological tests. Frequencies of performing DM self-care activities of adherence to diet, exercise, blood glucose monitoring, foot care and medications were queried to determine levels of self-care, and a recent glycohemoglobin was obtained to determine glycemic control. Main results were that cognitive function in domains of attention, executive function, mental processing speed, or verbal episodic memory, after controlling for modifiable and non-modifiable covariates, did not independently explain glycemic control or the frequency of DM self-care activity performance by rural adults with DM. The covariates cardiovascular risk and depression independently explained cognitive function, and depression independently explained self-care performance.PHDNursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137032/1/frankini_1.pd