Data-driven approaches for predicting asthma attacks in adults in primary care

Background Asthma attacks cause approximately 270 hospitalisations and four deaths per day in the United Kingdom (UK). Previous attempts to construct data-driven risk prediction models of asthma attacks have lacked clinical utility: either producing inaccurate predictions or requiring patient data which are not cost-effective to collect on a large scale (such as electronic monitoring device data). Electronic Health Record (EHR) use throughout the UK enables researchers to harness comprehensive and panoramic patient data, but their cleaning and pre-processing requires sophisticated empirical experimentation and data analytics approaches. My objectives were to appraise the previously utilised methods in asthma attack risk prediction modelling for feature extraction, model development, and model selection, and to train and test a model in Scottish EHRs. Methods In this thesis, I used a Scottish longitudinal primary care EHR dataset with linked secondary care records, to investigate the optimisation of an asthma attack risk prediction model. To inform the model, I refined methods for estimation of asthma medication adherence from EHRs, compared model training data enrichment procedures, and evaluated measures for validating model performance. After conducting a critical appraisal of the methods employed in the literature, I trained and tested four statistical learning algorithms for prediction in the next four weeks, i.e. logistic regression, naïve Bayes classification, random forests, and extreme gradient boosting, and validated model performance in an unseen hold-out dataset. Training data enrichment methods were compared across all algorithms to establish whether the sensitivity of estimating relatively uncommon event incidence, such as asthma attacks in the general asthma population, could be improved. Secondary event horizons were also examined, such as prediction in the next six months. Empirical experimentation established the balanced accuracy to be the most appropriate prediction model performance measure, and the calibration between estimated and observed risk was additionally assessed using the Area Under the Receiver-Operator Curve (AUC). Results Data were available for over 670,000 individuals, followed for up to 17 years (177,306 person-years in total). Binary prediction of asthma attacks in the following four-week period resulted in 1,203,476 data samples, of which 1% contained one or more attacks (12,193 total attacks). In the preliminary model selection phase, the random forest algorithm provided the best balance between accuracy in those with asthma attacks (sensitivity) and in those predicted to have attacks (positive predictive value) in the following four weeks. In an unseen data partition, the final random forest model, with optimised hyper-parameters, achieved an AUC of 0.91, and a balanced accuracy of 73.6% after the application of an optimised decision threshold. Accurate predictions were made for a median of 99.6% of those who did not go on to have attacks (specificity). As expected with rare event predictions, the sensitivity was lower at 47.7%, but this was well balanced with the positive predictive value of 48.9%. Furthermore, several of the secondary models, including predicting asthma attacks in the following 12 weeks, achieved state-of-the-art performance and still had high potential clinical utility. Conclusions I successfully developed an EHR-based model for predicting asthma attacks in the next four weeks. Accurately predicting asthma attacks occurrence may facilitate closer monitoring to ensure that preventative therapy is adequately managing symptoms, reinforce the need to keep abreast of triggers, and allow rescue treatments to be administered quickly when necessary

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities from Thursday, October 26, 2023

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities on October 26, 2023.https://corescholar.libraries.wright.edu/celebration_abstract_books/1001/thumbnail.jp

Federal Register

Daily publication of the U.S. Office of the Federal Register contains rules and regulations, proposed legislation and rule changes, and other notices, including "Presidential proclamations and Executive Orders, Federal agency documents having general applicability and legal effect, documents required to be published by act of Congress, and other Federal agency documents of public interest" (p. ii). Table of Contents starts on page iii

Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Compiles peer-reviewed research and literature reviews on issues regarding patient safety and quality of care, ranging from evidence-based practice, patient-centered care, and nurses' working conditions to critical opportunities and tools for improvement

Federal Register

Daily publication of the U.S. Office of the Federal Register contains rules and regulations, proposed legislation and rule changes, and other notices, including "Presidential proclamations and Executive Orders, Federal agency documents having general applicability and legal effect, documents required to be published by act of Congress, and other Federal agency documents of public interest" (p. ii). Table of Contents starts on page iii

Data Journeys in the Sciences

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research