Using machine learning methods to improve healthcare delivery in diabetes management

This dissertation includes three studies, all focusing on Analytics and Patients information for improving diabetes management, namely educating patients and early detection of comorbidities. In these studies, we develop topic modeling and artificial neural network to acquire, preprocess, model, and predict to minimize the burden on diabetic patients and healthcare providers.The first essay explores the usage of Text Analytics, an unsupervised machine learning model, utilizing the vast data available on social media to improve diabetes education of the patients in managing the condition. Mainly we show the applicability of topic modeling to identify the gaps in diabetes education content and the information and knowledge needs of the patients. While traditional methods of the content decision were based on a group of experts' contributions, our proposed methodology considers the questions raised on social forums for support to extend the education content.The second essay implements Deep Neural Networks on EHR data to assist the clinicians in rank ordering the potential comorbidities that the specific patient may develop in the future. This essay helps prioritize regular screening for comorbidities and rationalize the screening process to improve adherence and effectiveness. Our model prediction helps identify diabetic retinopathy and nephropathy patients with very high precision compared to other traditional methods. Essays 1 and 2 focus on Data Analytics as a research tool for managing a chronic disease in the healthcare environment.The third essay goes through the challenges and best practices of data preprocessing for Analytics studies in healthcare. This study explores the standard preprocessing methodologies and their impact in the case of healthcare data analytics. Highlights the relevant modifications and adaptations to the standards CRISP_DM process. The suggestions are based on past research and the experience obtained in the projects discussed earlier in the thesis.Overall, the dissertation highlights the importance of data analytics in healthcare for better managing and diagnosing chronic diseases. It unfolds the economic value of implementing state-of-the-art IT methods in healthcare, where EHR & IT are predominantly costly and difficult to implement. The dissertation covers ANN and text mining implementation for diabetes management

Sexual orientation and identity in diabetes health care: the experience of Type 2 diabetes among lesbian, queer, and women-loving women

This Master's Thesis reports on the experiences of Type 2 Diabetes of Lesbian, Queer, and Women-Loving Women. The thesis examines the impact of sexual orientation on experiences with diabetes, and how this chronic disease affects the way a woman views herself, her health, and her body image. Each participant presented her narrative and world views in regards to her diabetes health care and management, stress and trauma, and management of relationships. Through narrative analysis, I have revealed differing mechanisms of coping and explanatory models; the many women of this study selectively chose to be more open about her sexual orientation than her diabetes status

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Identifying risk patterns for suicide attempts in individuals with diabetes : a data-driven approach using LASSO regression

Diabetes is a major health concern in the United States, with 34.2 million Americans affected in 2020. Unfortunately, the risk of suicide is also elevated in individuals with diabetes, with around 90,000 people with diabetes committing suicide each year. People with type 1 diabetes are three to four times more likely to attempt suicide, and those with newly diagnosed type 2 diabetes are twice as likely to attempt suicide compared to the general population. However, poor mental health comorbidity is still neglected, and more recommendations are needed to support for people with diabetes. It is widely acknowledged that the comorbidity of depression with diabetes is considered a higher risk factor for suicide attempts Previous studies have used logistic regression to identify risk factors for suicide attempts in individuals with diabetes. However, this technique can be prone to overfitting when the number of variables is high. To address this issue, we used the LASSO (Least Absolute Shrinkage and Selection Operator), a regularization technique, to reduce overfitting in a logistic regression model. It works by adding a penalty term ([lambda]) to the log-likelihood function, which shrinks the estimates of the coefficients. This process allows LASSO to act as a feature selection method, effectively setting coefficients that contribute most to the error to zero. Because few studies have focused on un derstanding the relationship between suicide attempts and diabetes, we used association rule mining ARM an explainable rule based machine learning technique, for knowledge discovery to reveal previously unknown relationships between suicide attempts and diabetes. This approach has already proved useful in the medical field, where it has been applied to electronic health record (EHR) data to discover associations such as disease co-occurrences, drug-disease associations, and symptomatic patterns of disease. However, no previous studies have used ARM to determine risk factors and predict suicide attempts in people with diabetes. The aim of this dissertation is to identify patterns of risk factors for suicide attempts in individuals with diabetes, with the long term goal of developing a clinical decision support system that can be integrated into EHRs. This system would allow healthcare providers to identify patients with diabetes at high risk of suicide attempts and provide appropriate preventive measures during outpatient clinic visits. To achieve this goal, we have three specific aims: (1) to identify potential risk factors for suicide attempts in individuals with diabetes through a literature review; (2) to investigate risk factors for suicide attempts in individuals with diabetes using LASSO regression; (3) to identify risk patterns for suicide attempts in individuals with diabetes using association rule mining. In this dissertation, we have reviewed the literature and compiled a list of data elements for suicide attempts in people with diabetes. We then retrieved data on patients with diabetes from Cerner Real-World Data [trade mark]. LASSO regression was used for feature selection, and ARM was used for investigating the risk patterns. We discovered risk patterns that are understandable and practical for healthcare providers. The findings of this research can inform suicide prevention efforts for people with diabetes and contribute to improved mental health outcomes.Includes bibliographical references

Precision Medicine: Viable Pathways to Address Existing Research Gaps

Precision Medicine (PM) seeks to customize medical treatments for patients based on measurable and identifiable characteristics. Unlike personalized medicine, this effort is not intended to result in tailored care for each patient. Instead, this effort seeks to improve overall care within the medical domain by shifting the focus from one-size-fits-all care to optimized care for specified subgroups. In order for the benefits of PM to be expeditiously realized, the diverse skills sets of the scientific community must be brought to bear on the problem. This research effort explores the intersection of quality engineering (QE) and healthcare to outline how existing methodologies within the QE field could support existing PM research goals. Specifically this work examines how to determine the value of patient characteristics for use in disease prediction models with select machine learning algorithms, proposes a method to incorporate patient risk into treatment decisions through the development of performance functions, and investigates the potential impact of incorrect assumptions on estimation methods used in optimization models

Causal Pattern Mining in Highly Heterogeneous and Temporal EHRs Data

University of Minnesota Ph.D. dissertation. March 2017. Major: Computer Science. Advisor: Vipin Kumar. 1 computer file (PDF); ix, 112 pages.The World Health Organization (WHO) estimates that the total healthcare spending in the U.S. is around 18\% of its GDP for the year 2011. Even with such a high per-capita expenditure, the quality of healthcare in U.S. lags behind as compared to the healthcare in other industrialized countries. This inefficient state of the U.S. healthcare system is attributed to the current Fee-for-service (FFS) model. Under the FFS model, healthcare providers (doctors, hospitals) receive payments for every hospital visit or service rendered. The lack of coordination between the service providers and patient outcomes, leads to an increase in the costs associated with the healthcare management, as healthcare providers often recommend expensive treatments. Several legislations have been approved in the recent past to improve the overall U.S. healthcare management while simultaneously reducing the associated costs. The HITECH Act, proposes to spend close to \$30 billion dollars on creating a nationwide repository of electronic Health Records (EHRs). Such a repository would consist of patient attributes such as demographics, laboratories test results, vital information and diagnosis codes. It is hoped that this EHR repository will be a platform to improve care coordination between service providers and patients healthcare outcomes, reduce health disparities thereby improving the overall healthcare management system. Data collected and stored in the EHR (HITECH) and the need to improve care efficiency and outcome (ACT) would help to improve the current state of U.S. healthcare system. Data mining techniques in conjunction with EHRs can be used to develop novel clinical decision making tools, to analyze the prevalence and incidence of diseases and to evaluate the efficacy of existing clinical and surgical interventions. In this thesis we focus on two key aspects of EHR data, i.e. temporality and causation. This becomes more important considering that the temporal nature of EHRs data has not been fully exploited. Further, increasing amounts of clinical evidence suggest that temporal nature is important for the development of clinical decision making tools and techniques. Secondly, several research articles hint at the the presence of antiquated clinical guidelines which are still in practice. In this dissertation, we first describe EHR along with the following terminologies : temporality, causation and heterogeneity. Building on this, we then describe methodologies for extracting non-causal patterns in the absence of longitudinal data. Further, we describe methods to extract non-causal patterns in the presence of longitudinal data. We describe such methodologies in the context of Type-2 Diabetes Mellitus (T2DM). Furthermore, we describe techniques to extract simple and complex causal patterns from longitudinal data in the context of sepsis and T2DM. Finally, we conclude this dissertation, by providing a summary of our work along with future directions

Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare

Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. There is a noticeable opportunity to implement a learning health care system and data-driven healthcare to make better medical decisions, better personalized predictions; and more precise discovering of risk factors and their interactions. In this research we focus on data-driven approaches for personalized medicine. We propose a research framework which emphasizes on three main phases: 1) Predictive modeling, 2) Patient subgroup analysis and 3) Treatment recommendation. Our goal is to develop novel methods for each phase and apply them in real-world applications. In the fist phase, we develop a new predictive approach based on feature representation using deep feature learning and word embedding techniques. Our method uses different deep architectures (Stacked autoencoders, Deep belief network and Variational autoencoders) for feature representation in higher-level abstractions to obtain effective and more robust features from EMRs, and then build prediction models on the top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled one is scarce. We investigate the performance of representation learning through a supervised approach. We perform our method on different small and large datasets. Finally we provide a comparative study and show that our predictive approach leads to better results in comparison with others. In the second phase, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variables. Finally, in the third phase, we introduce a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we propose a simple yet effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African-Americans and white patients. The results indicate that our approach outperforms significantly than baseline models

The Struggle for Balance: Culture Care Worldview of Mexican Americans About Diabetes Mellitus

The purpose of this study was to describe, explain, and interpret perspectives, perceptions, meanings, symbols, and lifeways to explicate the culture care worldview about Diabetes Mellitus (DM) for Mexican American participants. Leininger\u27s Culture Care Diversity and Universality Theory served as an organizing framework Interviews were conducted with thirty Mexican American key participants without DM. Four phases of analysis of ethnonursing method revealed thirteen categories, five patterns and three themes. The categories were: Health; faith and religion; natural living; tranquility and stress; strong emotions; susto; immigration; life in US; family advice and support; cultural beliefs; treatments of diabetes; care; and communication. The patterns were a pattern of: Concern about DM with much confusion and uncertainties about the disease; maintaining balance and body defenses towards health; integrating self-care, generic and professional values in care; adaption to change and stressors; and valuing nursing and professional care. The themes American participants value balance and health yet have many uncertainties and concerns about Diabetes Mellitus that impact their culture care worldview; Mexican American participants\u27 culture care worldview of Diabetes Mellitus integrates self-care with generic and professional care values, beliefs and practices; and Mexican American participants\u27 culture care worldview of professional care of Diabetes Mellitus, emphasizes culturally acceptable, compassionate, personalized care, based on communication, mutual trust and respect, provided within the context of the family that supports the person\u27s struggle for balance, health, wellbeing and function. The Struggle for Balance: Culture Care Worldview of Mexican Americans about Diabetes Mellitus Pictorial Model, Hernandez © 2013 was abstracted by author, from study literature, findings and themes. Implications and recommendations for nursing theory, practice, education, policy and research were described