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      A Roadmap for Optimizing Asthma Care Management via Computational Approaches

      research-article
      , PhD 1 , , , RN, PhD 2
      (Reviewer), (Reviewer)
      JMIR Medical Informatics
      JMIR Publications
      patient care management, clinical decision support, machine learning

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          Abstract

          Asthma affects 9% of Americans and incurs US $56 billion in cost, 439,000 hospitalizations, and 1.8 million emergency room visits annually. A small fraction of asthma patients with high vulnerabilities, severe disease, or great barriers to care consume most health care costs and resources. An effective approach is urgently needed to identify high-risk patients and intervene to improve outcomes and to reduce costs and resource use. Care management is widely used to implement tailored care plans for this purpose, but it is expensive and has limited service capacity. To maximize benefit, we should enroll only patients anticipated to have the highest costs or worst prognosis. Effective care management requires correctly identifying high-risk patients, but current patient identification approaches have major limitations. This paper pinpoints these limitations and outlines multiple machine learning techniques to address them, providing a roadmap for future research.

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          Most cited references80

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          Launching PCORnet, a national patient-centered clinical research network

          The Patient-Centered Outcomes Research Institute (PCORI) has launched PCORnet, a major initiative to support an effective, sustainable national research infrastructure that will advance the use of electronic health data in comparative effectiveness research (CER) and other types of research. In December 2013, PCORI's board of governors funded 11 clinical data research networks (CDRNs) and 18 patient-powered research networks (PPRNs) for a period of 18 months. CDRNs are based on the electronic health records and other electronic sources of very large populations receiving healthcare within integrated or networked delivery systems. PPRNs are built primarily by communities of motivated patients, forming partnerships with researchers. These patients intend to participate in clinical research, by generating questions, sharing data, volunteering for interventional trials, and interpreting and disseminating results. Rapidly building a new national resource to facilitate a large-scale, patient-centered CER is associated with a number of technical, regulatory, and organizational challenges, which are described here.
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            When patient activation levels change, health outcomes and costs change, too.

            Patient engagement has become a major focus of health reform. However, there is limited evidence showing that increases in patient engagement are associated with improved health outcomes or lower costs. We examined the extent to which a single assessment of engagement, the Patient Activation Measure, was associated with health outcomes and costs over time, and whether changes in assessed activation were related to expected changes in outcomes and costs. We used data on adult primary care patients from a single large health care system where the Patient Activation Measure is routinely used. We found that results indicating higher activation in 2010 were associated with nine out of thirteen better health outcomes-including better clinical indicators, more healthy behaviors, and greater use of women's preventive screening tests-as well as with lower costs two years later. Changes in activation level were associated with changes in over half of the health outcomes examined, as well as costs, in the expected directions. These findings suggest that efforts to increase patient activation may help achieve key goals of health reform and that further research is warranted to examine whether the observed associations are causal.
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              SMOTE: Synthetic Minority Over-sampling Technique

              An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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                Author and article information

                Contributors
                Journal
                JMIR Med Inform
                JMIR Med Inform
                JMI
                JMIR Medical Informatics
                JMIR Publications (Toronto, Canada )
                2291-9694
                Jul-Sep 2017
                26 September 2017
                : 5
                : 3
                : e32
                Affiliations
                [1] 1 Department of Biomedical Informatics and Medical Education University of Washington Seattle, WA United States
                [2] 2 College of Nursing University of Utah Salt Lake City, UT United States
                Author notes
                Corresponding Author: Gang Luo gangluo@ 123456cs.wisc.edu
                Author information
                http://orcid.org/0000-0001-7217-4008
                http://orcid.org/0000-0002-6568-4031
                Article
                v5i3e32
                10.2196/medinform.8076
                5635229
                28951380
                85313e33-2b12-438f-992c-323028abba2b
                ©Gang Luo, Katherine Sward. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.09.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

                History
                : 20 May 2017
                : 6 July 2017
                : 9 July 2017
                : 14 August 2017
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                patient care management,clinical decision support,machine learning

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