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      Health intelligence: how artificial intelligence transforms population and personalized health

      editorial

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          Abstract

          Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.

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          PopHR: a knowledge-based platform to support integration, analysis, and visualization of population health data

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            Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits

            The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%. The analysis has been performed over an integrated dataset consisting of individual-level patient information such as gender, race, insurance type, and age, along with ZIP code-level sociomarkers such as poverty level, blight prevalence, and housing quality. Using this uniquely integrated database, we then compare the traditional biomarker-based risk model and the sociomarker-based risk model. A biomarker-based predictive model yields an accuracy of 65% and the sociomarker-based model predicts with an accuracy of 61%. Without knowing specific symptom-related features, the sociomarker-based model can correctly predict two out of three patients at risk. We systematically show that sociomarkers play an important role in predicting health outcomes at the individual level in pediatric asthma cases. Additionally, by merging multiple data sources with detailed neighborhood-level data, we directly measure the importance of residential conditions for predicting individual health outcomes.
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              A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis

              Accurate prognosis is fundamental in planning an appropriate therapy for cancer patients. Consequent to the heterogeneity of the disease, intra- and inter-pathologist variability, and the inherent limitations of current pathological reporting systems, patient outcome varies considerably within similarly staged patient cohorts. This is particularly true when classifying stage II colorectal cancer patients using the current TNM guidelines. The aim of the present work is to address this problem through the use of machine learning. In particular, we introduce a data driven framework which makes use of a large number of diverse types of features, readily collected from immunofluorescence imagery. Its outstanding performance in predicting mortality in stage II patients (AUROC = 0:94), exceeds that of current clinical guidelines such as pT stage (AUROC = 0:65), and is demonstrated on a cohort of 173 colorectal cancer patients.
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                Author and article information

                Contributors
                ashabann@uthsc.edu
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                2 October 2018
                2 October 2018
                2018
                : 1
                : 53
                Affiliations
                [1 ]ISNI 0000 0004 0386 9246, GRID grid.267301.1, Department of Pediatrics, , University of Tennessee Health Science Center – Oak Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, ; Memphis, TN USA
                [2 ]ISNI 0000000419368657, GRID grid.17635.36, School of Nursing, , University of Minnesota, ; Minneapolis, MN USA
                [3 ]Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC USA
                Author information
                http://orcid.org/0000-0003-2047-4759
                http://orcid.org/0000-0003-2060-5878
                Article
                58
                10.1038/s41746-018-0058-9
                6550150
                31304287
                e81546f5-f38d-4ebc-9816-1c6e37df1134
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 August 2018
                : 29 August 2018
                : 30 August 2018
                Categories
                Editorial
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                © The Author(s) 2018

                population screening,research management
                population screening, research management

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