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Each year the National Institute of Health spends over 12 billion dollars on patient related medical research. Accurately classifying patients into categories representing disease, exposures, or other medical conditions important to a study is critical when conducting patient-related research. Without rigorous characterization of patients, also referred to as phenotyping, relationships between exposures and outcomes could not be assessed, thus leading to non-reproducible study results. Developing tools to extract information from the electronic health record (EHR) and methods that can augment a team's perspective or reasoning capabilities to improve the accuracy of a phenotyping model is the focus of this research. This thesis demonstrates that employing state-of-the-art computational methods makes it possible to accurately phenotype patients based entirely on data found within an EHR, even though the EHR data is not entered for that purpose. Three studies using the Marshfield Clinic EHR are described herein to support this research. The first study used a multi-modal phenotyping approach to identify cataract patients for a genome-wide association study. Structured query data mining, natural language processing and optical character recognition where used to extract cataract attributes from the data warehouse, clinical narratives and image documents. Using these methods increased the yield of cataract attribute information 3-fold while maintaining a high degree of accuracy. The second study demonstrates the use of relational machine learning as a computational approach for identifying unanticipated adverse drug reactions (ADEs). Matching and filtering methods adopted were applied to training examples to enhance relational learning for ADE detection. The final study examines relational machine learning as a possible alternative for EHR-based phenotyping. Several innovations including identification of positive examples using ICD-9 codes and infusing negative examples with borderline positive examples were employed to minimize reference expert effort, time and even to some extent possible bias. The study found that relational learning performed significantly better than two popular decision tree learning algorithms for phenotyping when evaluating area under the receiver operator characteristic curve. Findings from this research support my thesis that states: Innovative use of computational methods makes it possible to more accurately characterize research subjects based on EHR data.
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
The purpose of the book is to provide an overview of clinical research (types), activities, and areas where informatics and IT could fit into various activities and business practices. This book will introduce and apply informatics concepts only as they have particular relevance to clinical research settings.
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
This volume presents the proceedings of the International Conference on Biomedical and Health Informatics (ICBHI). The conference was a new special topic conference and a common initiative by the International Federation of Medical and Biological Engineering (IFMBE) and IEEE Engineering in Medicine and Biology Society (IEEE- EMBS). BHI2015 was held in Haikou, China, 8-10 October 2015. The main theme of the BHI2015 is “The Convergence: Integrating Information and Communication Technologies with Biomedicine for Global Health”. The ICBHI2015 proceedings examine enabling technologies of sensors, devices and systems that optimize the acquisition, transmission, processing, storage, retrieval, use of biomedical and health information as well as to report novel clinical applications of health information systems and the deployment of m-Health, e-Health, u-Health, p-Health and Telemedicine.
The biobank era of genomics has ushered in a multitude of opportunities for precision medicine research. In particular, biobanks connected to electronic health records (EHR) provide rich phenotype information used to study to clinical phenome. First, I describe two computational methods designed to infer the genetic architecture of complex traits using biobank-scale data. Both methods are based on Markov Chain Monte Carlo techniques. Next, I provide an overview of the UCLA ATLAS Community Health Initiative (ATLAS), an EHR-linked biobank embedded within UCLA Health. Using this data set, I explore the role of genetic ancestry in common disease risk across the UCLA patient population. Next, I include a review of how race, ethnicity, and genetic ancestry are utilized in the field of EHR- linked biobanks. Finally, I propose an EHR-based algorithm, called PheNet, which identifies undiagnosed patients with Common Variable Immunodeficiency Disorders and demonstrate its application across a total of 5 University of California Health systems.
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques. Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis. Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence Helps readers analyze and do advanced research in specialty healthcare applications Includes links to websites, videos, articles and other online content to expand and support primary learning objectives