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      Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

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          Abstract

          Background

          Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

          Objective

          To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

          Methods

          We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

          Results

          We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

          Conclusions

          Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

          International Registered Report Identifier (IRRID)

          PRR1-10.2196/27065

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          Most cited references184

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          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                May 2021
                18 May 2021
                : 10
                : 5
                : e27065
                Affiliations
                [1 ] Department of Biomedical Informatics and Medical Education University of Washington Seattle, WA United States
                [2 ] Department of Pediatrics University of Utah Salt Lake City, UT United States
                [3 ] College of Nursing University of Utah Salt Lake City, UT United States
                [4 ] Care Transformation and Information Systems Intermountain Healthcare West Valley City, UT United States
                [5 ] Department of Research & Evaluation Kaiser Permanente Southern California Pasadena, CA United States
                Author notes
                Corresponding Author: Gang Luo gangluo@ 123456cs.wisc.edu
                Author information
                https://orcid.org/0000-0001-7217-4008
                https://orcid.org/0000-0002-0912-5227
                https://orcid.org/0000-0001-6576-3699
                https://orcid.org/0000-0002-7982-9625
                https://orcid.org/0000-0001-8274-0309
                https://orcid.org/0000-0002-8954-8288
                Article
                v10i5e27065
                10.2196/27065
                8170556
                34003134
                4e8ce382-1b72-499b-944e-7062e981d270
                ©Gang Luo, Bryan L Stone, Xiaoming Sheng, Shan He, Corinna Koebnick, Flory L Nkoy. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 18.05.2021.

                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 Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 16 January 2021
                : 5 April 2021
                : 12 April 2021
                : 19 April 2021
                Categories
                Protocol
                Protocol

                asthma,chronic obstructive pulmonary disease,decision support techniques,forecasting,machine learning

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