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      Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives—a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

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          Abstract

          Objectives

          This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field.

          Methods and Results

          This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals.

          Discussion

          Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.

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          Most cited references144

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          Big data and machine learning algorithms for health-care delivery

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            Machine Learning for Precision Psychiatry: Opportunities and Challenges.

            The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, cross-validation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patients can better predict treatment outcomes than DSM/ICD diagnoses can. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection, and dosage adjustment to reduce the burden of disease. This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice.
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              Is Open Access

              Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010

              Global data sets on the geographic distribution of livestock are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessment and epidemiology. We present a new version of the Gridded Livestock of the World (GLW 3) database, reflecting the most recently compiled and harmonized subnational livestock distribution data for 2010. GLW 3 provides global population densities of cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in each land pixel at a spatial resolution of 0.083333 decimal degrees (approximately 10 km at the equator). They are accompanied by detailed metadata on the year, spatial resolution and source of the input census data. Two versions of each species distribution are produced. In the first version, livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates (dasymetric weighting). In the second version, animal numbers are distributed homogeneously with equal densities within their census polygons (areal weighting) to provide spatial data layers free of any assumptions linking them to other spatial variables.
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                Author and article information

                Journal
                JAMIA Open
                JAMIA Open
                jamiaoa
                JAMIA Open
                Oxford University Press
                2574-2531
                July 2020
                11 April 2020
                11 April 2020
                : 3
                : 2
                : 306-317
                Affiliations
                [o1 ] Association for Veterinary Informatics , Dixon, California, USA
                [o2 ] VCA Inc. , Health Technology & Informatics, Los Angeles, California, USA
                [o3 ] Fauna Bio Inc. , Berkeley, California, USA
                [o4 ] Veterinary Medical Databases , Columbia, Missouri, USA
                [o5 ] Department of Infectious diseases and HIV medicine , Drexel University College of Medicine, Philadelphia, Pennsylvania, USA
                [o6 ] Department of Clinical Sciences , Colorado State University, Fort Collins, Colorado, USA
                Author notes
                Corresponding Author: Jonathan L. Lustgarten, MS, PhD, VMD, VCA Inc., Health Technology & Informatics, 12401 W Olympic Blvd, Los Angeles, CA 90064, USA; jonathan.lustgarten@ 123456vca.com
                Author information
                http://orcid.org/0000-0003-4386-3044
                Article
                ooaa005
                10.1093/jamiaopen/ooaa005
                7382640
                32734172
                6c7cbcfa-1a03-4f9a-b5da-7271613fb2a8
                © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 14 August 2019
                : 26 December 2019
                : 26 February 2020
                Page count
                Pages: 12
                Funding
                Funded by: Association for Veterinary Informatics;
                Funded by: CTSA One Health Alliance;
                Categories
                Review
                AcademicSubjects/SCI01530
                AcademicSubjects/MED00010
                AcademicSubjects/SCI01060

                informatics,one health,translational science,veterinary medicine,medicine

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