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Review Article| Volume 39, ISSUE 2, P319-331, June 2019

Machine Learning and Other Emerging Decision Support Tools

Published:March 28, 2019DOI:https://doi.org/10.1016/j.cll.2019.01.010

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      References

        • Baron J.M.
        • Dighe A.S.
        The role of informatics and decision support in utilization management.
        Clin Chim Acta. 2014; 427: 196-201
        • Kim J.Y.
        • Dzik W.H.
        • Dighe A.S.
        • et al.
        Utilization management in a large urban academic medical center: a 10-year experience.
        Am J Clin Pathol. 2011; 135: 108-118
        • Baron J.M.
        • Dighe A.S.
        Computerized provider order entry in the clinical laboratory.
        J Pathol Inform. 2011; 2: 35
        • Grisson R.
        • Kim J.Y.
        • Brodsky V.
        • et al.
        A novel class of laboratory middleware. Promoting information flow and improving computerized provider order entry.
        Am J Clin Pathol. 2010; 133: 860-869
        • Henricks W.H.
        • Wilkerson M.L.
        • Castellani W.J.
        • et al.
        Pathologists as stewards of laboratory information.
        Arch Pathol Lab Med. 2015; 139: 332-337
        • Sepulveda J.L.
        • Young D.S.
        The ideal laboratory information system.
        Arch Pathol Lab Med. 2013; 137: 1129-1140
        • Baron J.M.
        • Lewandrowski K.B.
        • Kamis I.K.
        • et al.
        A novel strategy for evaluating the effects of an electronic test ordering alert message: optimizing cardiac marker use.
        J Pathol Inform. 2012; 3: 3
        • Baron J.M.
        • Cheng X.S.
        • Bazari H.
        • et al.
        Enhanced creatinine and estimated glomerular filtration rate reporting to facilitate detection of acute kidney injury.
        Am J Clin Pathol. 2015; 143: 42-49
        • Louis D.N.
        • Feldman M.
        • Carter A.B.
        • et al.
        Computational pathology.
        Arch Pathol Lab Med. 2016; 41: 41-50
        • Louis D.N.
        • Gerber G.K.
        • Baron J.M.
        • et al.
        Computational pathology: an emerging definition.
        Arch Pathol Lab Med. 2014; 138: 1133-1138
        • Shirts B.H.
        • Jackson B.R.
        • Baird G.S.
        • et al.
        Clinical laboratory analytics: challenges and promise for an emerging discipline.
        J Pathol Inform. 2015; 6: 9
        • Bastanlar Y.
        • Ozuysal M.
        Introduction to machine learning.
        Methods Mol Biol. 2014; 1107: 105-128
        • Hand D.J.
        Aspects of data ethics in a changing world: where are we now?.
        Big Data. 2018; 6: 176-190
        • Mittelstadt B.D.
        • Floridi L.
        The ethics of big data: current and foreseeable issues in biomedical contexts.
        Sci Eng Ethics. 2016; 22: 303-341
        • Al-Hablani B.
        The use of automated SNOMED CT clinical coding in clinical decision support systems for preventive care.
        Perspect Health Inf Manag. 2017; 14: 1f
        • Ciolko E.
        • Lu F.
        • Joshi A.
        Intelligent clinical decision support systems based on SNOMED CT.
        Conf Proc IEEE Eng Med Biol Soc. 2010; 2010: 6781-6784
        • Cornet R.
        • de Keizer N.
        Forty years of SNOMED: a literature review.
        BMC Med Inform Decis Mak. 2008; 8: S2
        • Ahmadian L.
        • van Engen-Verheul M.
        • Bakhshi-Raiez F.
        • et al.
        The role of standardized data and terminological systems in computerized clinical decision support systems: literature review and survey.
        Int J Med Inform. 2011; 80: 81-93
        • Luo Y.
        • Szolovits P.
        • Dighe A.S.
        • et al.
        Using machine learning to predict laboratory test results.
        Am J Clin Pathol. 2016; 145: 778-788
        • Luo Y.
        • Szolovits P.
        • Dighe A.S.
        • et al.
        3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data.
        J Am Med Inform Assoc. 2018; 25: 645-653
        • Waljee A.K.
        • Mukherjee A.
        • Singal A.G.
        • et al.
        Comparison of imputation methods for missing laboratory data in medicine.
        BMJ Open. 2013; 3 ([pii:e002847])
        • Stekhoven D.J.
        • Buhlmann P.
        MissForest–non-parametric missing value imputation for mixed-type data.
        Bioinformatics. 2012; 28: 112-118
        • van Buuren S.
        • Boshuizen H.C.
        • Knook D.L.
        Multiple imputation of missing blood pressure covariates in survival analysis.
        Stat Med. 1999; 18: 681-694
        • van Buuren S.
        • Groothuis-Oudshoorn K.
        Mice: multivariate imputation by chained equations in R.
        J Stat Softw. 2011; 45: 1-67
        • Baron J.M.
        • Mermel C.H.
        • Lewandrowski K.B.
        • et al.
        Detection of preanalytic laboratory testing errors using a statistically guided protocol.
        Am J Clin Pathol. 2012; 138: 406-413
        • Rosenbaum M.W.
        • Baron J.M.
        Using machine learning-based multianalyte delta checks to detect wrong blood in tube errors.
        Am J Clin Pathol. 2018; 150: 555-566
        • Mandel J.C.
        • Kreda D.A.
        • Mandl K.D.
        • et al.
        SMART on FHIR: a standards-based, interoperable apps platform for electronic health records.
        J Am Med Inform Assoc. 2016; 23: 899-908
        • Aronson S.
        • Mahanta L.
        • Ros L.L.
        • et al.
        Information technology support for clinical genetic testing within an Academic Medical center.
        J Pers Med. 2016; 6 ([pii:E4])
        • Aronson S.J.
        • Rehm H.L.
        Building the foundation for genomics in precision medicine.
        Nature. 2015; 526: 336-342
        • Mandl K.D.
        • Mandel J.C.
        • Kohane I.S.
        Driving innovation in health systems through an apps-based information economy.
        Cell Syst. 2015; 1: 8-13
        • Boussadi A.
        • Zapletal E.
        A fast healthcare interoperability resources (FHIR) layer implemented over i2b2.
        BMC Med Inform Decis Mak. 2017; 17: 120
        • Health Level Seven International
        FHIR.
        (Available at:) (Accessed November 12, 2018)