Utilizing Temporal Information in The EHR for Developing a Novel Continuous Prediction Model

Type 2 diabetes mellitus (T2DM) is a nation-wide prevalent chronic condition, which includes direct and indirect healthcare costs. T2DM, however, is a preventable chronic condition based on previous clinical research. Many prediction models were based on the risk factors identified by clinical trials. One of the major tasks of the T2DM prediction models is to estimate the risks for further testing by HbA1c or fasting plasma glucose to determine whether the patient has or does not have T2DM because nation-wide screening is not cost-effective. Those models had substantial limitations on data quality, such as missing values. In this dissertation, I tested the conventional models which were based on the most widely used risk factors to predict the possibility of developing T2DM. The AUC was an average of 0.5, which implies the conventional model cannot be used to screen for T2DM risks. Based on this result, I further implemented three types of temporal representations, including non-temporal representation, interval-temporal representation, and continuous-temporal representation for building the T2DM prediction model. According to the results, continuous-temporal representation had the best performance. Continuous-temporal representation was based on deep learning methods. The result implied that the deep learning method could overcome the data quality issue and could achieve better performance. This dissertation also contributes to a continuous risk output model based on the seq2seq model. This model can generate a monotonic increasing function for a given patient to predict the future probability of developing T2DM. The model is workable but still has many limitations to overcome. Finally, this dissertation demonstrates some risks factors which are underestimated and are worthy for further research to revise the current T2DM screening guideline. The results were still preliminary. I need to collaborate with an epidemiologist and other fields to verify the findings. In the future, the methods for building a T2DM prediction model can also be used for other prediction models of chronic conditions

Healthy snacks consumption and the Theory of Planned Behaviour. The role of anticipated regret

Two empirical studies explored the role of anticipated regret (AR) within the Theory of Planned Behavior (TPB) framework (Ajzen, 1991), applied to the case of healthy snacks consumption. AR captures affective reactions and it can be defined as an unpleasant emotion experienced when people realize or imagine that the present situation would be better if they had made a different decision. In this research AR refers to the expected negative feelings for not having consumed healthy snacks (i.e., inaction regret). The aims were: a) to test whether AR improves the TPB predictive power; b) to analyze whether it acts as moderator within the TPB model relationships. Two longitudinal studies were conducted. Target behaviors were: consumption of fruit and vegetables as snacks (Study 1); consumption of fruit as snacks (Study 2). At time 1, the questionnaire included measures of intention and its antecedents, according to the TPB. Both the affective and evaluative components of attitude were assessed. At time 2, self-reported consumption behaviors were surveyed. Two convenience samples of Italian adults were recruited. In hierarchical regressions, the TPB variables were added at the first step; AR was added at the second step, and the interactions at the last step. Results showed that AR significantly improved the TPB ability to predict both intentions and behaviours, also after controlling for intention. In both studies AR moderated the effect of affective attitude on intention: affective attitude was significant only for people low in AR

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

Influences on Pregnancy: An Exploration of Maternal Discrimination, an Alternative Model of Prenatal Care and Health Information Online

In the United States metrics of perinatal health lag far behind peer countries and is characterized by stark disparities. The studies that make up this dissertation seek to elucidate factors influencing perinatal health. The first and second chapters provide an introduction and extensive review of the literature of factors contributing to perinatal health with specific focus on discrimination and perinatal health; group prenatal care and digital pregnancy health information. The third chapter introduces the methodology to be used by each of the following studies. Subsequent chapters are formatted as individual manuscripts, each presenting background, methodology, results, and discussion. The fourth chapter (Manuscript 1) sought to explore pregnant persons intersectional experience of discrimination and the association with adverse perinatal health outcomes. This study was a secondary analysis of data collected in a randomized controlled trial of pregnant persons at a single practice (CRADLE study). Latent class analysis was used to identify distinct subgroups of discrimination experience based on patterns of response to Everyday Discrimination Scale items and between subgroup differences in rate of adverse perinatal health outcomes examined utilizing a BCH three-step approach. Four discrimination subgroups were identified among racial and ethnic groups. The general discrimination latent class was associated with elevated risk of postpartum depression symptoms (among Black and White participants) and low infant birthweight (among White participants) relative to the no discrimination latent class. No significant subgroup differences were observed among Hispanic participants. Findings demonstrate the importance of intersectional discrimination exposure in shaping perinatal health. The fifth chapter (Manuscript 2) applied a concurrent mixed methods approach in the examination of patient characteristics associated with group prenatal care and the exploration of patient experiences in group compared to individual prenatal care. This study was a secondary analysis of data collected in the CRADLE study, as well as patient interviews collected in a coordinated process evaluation. The association of patient sociodemographic, psychosocial and health characteristics with group prenatal care session attendance were examined using zero-inflated poison regression models. Thematic analysis of patient interviews was conducted. Varied patient characteristics were found to be associated with session attendance. Group prenatal care was identified to offer alternative opportunities for education, engagement, and peer support. Findings offer insight into model modifications, recruitment, and retention strategies. The sixth chapter (Manuscript 3) utilized topic modeling to describe topics of discussion in online pregnancy forums. Data was gathered from three active online pregnancy forums for a one-year period. Discussion threads were processed, converted to a document term matrix and Latent Dirichlet Allocation performed. Forty-six percent of threads were determined to be health related. The largest health-related topic categories included fertility, planning for delivery, miscarriage and pregnancy symptoms. Findings offer insight into dominant health related topics being discussed among online peer communities, potentially reflecting unmet information needs during pregnancy

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Prevention and Management of Frailty

It is important to prevent and manage the frailty of the elderly because their muscle strength and physical activity decrease in old age, making them prone to falling, depression, and social isolation. In the end, they need to be admitted to a hospital or a nursing home. When successful aging fails and motor ability declines due to illness, malnutrition, or reduced activity, frailty eventually occurs. Once frailty occurs, people with frailty do not have the power to exercise or the power to move. The functions of the heart and muscles are deteriorated more rapidly when they are not used. Consequently, frailty goes through a vicious cycle. As one’s physical fitness is deteriorated, the person has less power to exercise, poorer cognitive functions, and inferior nutrition intake. Consequently, the whole body of the person deteriorates. Therefore, in addition to observational studies to identify risk factors for preventing aging, various intervention studies have been conducted to develop exercise programs and apply them to communities, hospitals, and nursing homes for helping the elderly maintain healthy lives. Until now, most aging studies have focused on physical frailty. However, social frailty and cognitive frailty affect senile health negatively just as much as physical frailty. Nevertheless, little is known about social frailty and cognitive frailty. This special issue includes original experimental studies, reviews, systematic reviews, and meta-analysis studies on the prevention of senescence (physical senescence, cognitive senescence, social senescence), high-risk group detection, differentiation, and intervention

Computational Methods for Analyzing Health News Coverage

Researchers that investigate the media's coverage of health have historically relied on keyword searches to retrieve relevant health news coverage, and manual content analysis methods to categorize and score health news text. These methods are problematic. Manual content analysis methods are labor intensive, time consuming, and inherently subjective because they rely on human coders to review, score, and annotate content. Retrieving relevant health news coverage using keywords can be challenging because manually defining an optimal keyword query, especially for complex health topics and media analysis concepts, can be very difficult, and the optimal query may vary based on when the news was published, the type of news published, and the target audience of the news coverage. This dissertation research investigated computational methods that can assist health news investigators by facilitating these tasks. The first step was to identify the research methods currently used by investigators, and the research questions and health topics researchers tend to investigate. To capture this information an extensive literature review of health news analyses was performed. No literature review of this type and scope could be found in the research literature. This review confirmed that researchers overwhelmingly rely on manual content analysis methods to analyze the text of health news coverage, and on the use of keyword searching to identify relevant health news articles. To investigate the use of computational methods for facilitating these tasks, classifiers that categorize health news on relevance to the topic of obesity, and on their news framing were developed and evaluated. The obesity news classifier developed for this dissertation outperformed alternative methods, including searching based on keyword appearance. Classifying on the framing of health news proved to be a more difficult task. The news framing classifiers performed well, but the results suggest that the underlying features of health news coverage that contribute to the framing of health news are a richer and more useful source of framing information rather than binary news framing classifications. The third step in this dissertation was to use the findings of the literature review and the classifier studies to design the SalientHealthNews system. The purpose of SalientHealthNews is to facilitate the use of computational and data mining techniques for health news investigation, hypothesis testing, and hypothesis generation. To illustrate the use of SalientHealthNews' features and algorithms, it was used to generate preliminary data for a study investigating how framing features vary in health and obesity news coverage that discusses populations with health disparities. This research contributes to the study of the media's coverage of health by providing a detailed description of how health news is studied and what health news topics are investigated, then by demonstrating that certain tasks performed in health news analyses can be facilitated by computational methods, and lastly by describing the design of a system that will facilitate the use of computational and data mining techniques for the study of health news. These contributions should further the study of health news by expanding the methods available to health news analysis researchers. This will lead to researchers being better equipped to accurately and consistently evaluate the media's coverage of health. Knowledge of the quality of health news coverage should in turn lead to better informed health journalists, healthcare providers, and healthcare consumers, ultimately improving individual and public health