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Rapid increase in the generation of digital clinical and medical data created a tremendous interest in using machine learning in medical research. Advanced computational methods have considerable promise for improving the accuracy and efficiency of medical practices and patients' outcomes. In my work, I demonstrate the application of machine learning in improving various stages of patient care through automated population screening, health risk evaluation and informed intervention. First, I developed a fast, easy and cost effective method to screen for carriers of the FMR1 premutation using machine learning models by analyzing five-minute speech samples. The resultant method is fully automated, does not rely on any manual coding and is able to process hundreds of speech samples in a few seconds. Without using any genetic information, the algorithm is able to identify individuals with the FMR1 premutation with a high degree of accuracy. Next, leveraging the electronic health records from the Marshfield Clinic, we created the first population-based FMR1-informed biobank to examine patterns of health problems in individuals with the premutation. We applied machine learning on diagnostic codes to discriminate premutation carriers from the general population. Then we examined individual clinical phenotypes to identify primary phenotypes associated with the FMR1 premutation. Our population-based, unbiased, double-blinded approach enabled us to not only confirm the known phenotypes associated with the premutation, we also discovered new phenotypes that have never been identified as characteristic of these individuals. Knowledge of the clinical risk associated with this genetic variant is critical for premutation carriers, families and clinicians, and has important implications for public health. Finally, I developed a new method to screen "expressed emotion", which is a measure of a family's emotional climate and a key component in predicting relapse in patients with schizophrenia or other disabilities. Our approach replaces the time-consuming, cumbersome and costly process of evaluating expressed emotion manually with a fully automatic framework, which relies on natural language processing and machine learning methods. The ability to rapidly screen expressed emotion in the clinic setting can enable timely psychoeducational intervention for families, leading to lower rates of relapse and more effective treatment in patients.
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.
Psychiatrists and neuroscientists discuss the potential of computational approaches to address problems in psychiatry including diagnosis, treatment, and integration with neurobiology. Modern psychiatry is at a crossroads, as it attempts to balance neurological analysis with psychological assessment. Computational neuroscience offers a new lens through which to view such thorny issues as diagnosis, treatment, and integration with neurobiology. In this volume, psychiatrists and theoretical and computational neuroscientists consider the potential of computational approaches to psychiatric issues. This unique collaboration yields surprising results, innovative synergies, and novel open questions. The contributors consider mechanisms of psychiatric disorders, the use of computation and imaging to model psychiatric disorders, ways that computation can inform psychiatric nosology, and specific applications of the computational approach. Contributors Susanne E. Ahmari, Huda Akil, Deanna M. Barch, Matthew Botvinick, Michael Breakspear, Cameron S. Carter, Matthew V. Chafee, Sophie Denève, Daniel Durstewitz, Michael B. First, Shelly B. Flagel, Michael J. Frank, Karl J. Friston, Joshua A. Gordon, Katia M. Harlé, Crane Huang, Quentin J. M. Huys, Peter W. Kalivas, John H. Krystal, Zeb Kurth-Nelson, Angus W. MacDonald III, Tiago V. Maia, Robert C. Malenka, Sanjay J. Mathew, Christoph Mathys, P. Read Montague, Rosalyn Moran, Theoden I. Netoff, Yael Niv, John P. O'Doherty, Wolfgang M. Pauli, Martin P. Paulus, Frederike Petzschner, Daniel S. Pine, A. David Redish, Kerry Ressler, Katharina Schmack, Jordan W. Smoller, Klaas Enno Stephan, Anita Thapar, Heike Tost, Nelson Totah, Jennifer L. Zick
The Pacific Symposium on Biocomputing (PSB) 2019 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2019 will be held on January 3 - 7, 2019 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2019 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
This book features original papers from the 3rd International Conference on Smart IoT Systems: Innovations and Computing (SSIC 2021), presenting scientific work related to smart solution concepts. It discusses scientific works related to smart solutions concept in the context of computational collective intelligence consisted of interaction between smart devices for smart environments and interactions. Thanks to the high-quality content and the broad range of the topics covered, the book appeals to researchers pursuing advanced studies.
Comprehensively presents the foundations and leading application research in medical informatics/biomedicine. The concepts and techniques are illustrated with detailed case studies. Authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science University. Related Springer title, Shortliffe: Medical Informatics, has sold over 8000 copies The title will be positioned at the upper division and graduate level Medical Informatics course and a reference work for practitioners in the field.
Precision Public Health is a new and rapidly evolving field, that examines the application of new technologies to public health policy and practice. It draws on a broad range of disciplines including genomics, spatial data, data linkage, epidemiology, health informatics, big data, predictive analytics and communications. The hope is that these new technologies will strengthen preventive health, improve access to health care, and reach disadvantaged populations in all areas of the world. But what are the downsides and what are the risks, and how can we ensure the benefits flow to those population groups most in need, rather than simply to those individuals who can afford to pay? This is the first collection of theoretical frameworks, analyses of empirical data, and case studies to be assembled on this topic, published to stimulate debate and promote collaborative work.
Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions Provides background, milestone and examples of precision medicine Outlines the paradigm shift towards precision medicine driven by value-based systems Discusses future applications of precision medicine research using AI Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine
This book presents an innovative approach to clinical assessment in psychiatry based on a number of psychopathological dimensions with a presumed underlying pathophysiology, that are related to fundamental phenomenological aspects and lie on a continuum from normality to pathology. It is described how the evaluation of these dimensions with a specific, validated rapid assessment instrument could easily integrate and enrich the classical diagnostic DSM-5 or ICD-10 assessment. The supplemental use of this dimensional approach can better capture the complexity underlying current categories of mental illness. The findings from a large patient sample suggest how this assessment could give a first glance at how variable and multifaceted the psychopathological components within a single diagnostic category can be, and thereby optimise diagnosis and treatment choices. Being short and easy to complete, this dimensional assessment can be done in a busy clinical setting, during an ordinary psychiatric visit, and in an acute clinical context, with limited effort by a minimally trained clinician. Therefore, it provides interesting and useful information without additional costs, and allows research work to be performed even in difficult settings.