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Rescue inhaler usage prediction in smart asthma management systems using joint mixed effects logistic regression model

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  • Junbo Son
  • Patricia Flatley Brennan
  • Shiyu Zhou

Abstract

Asthma is a very common and chronic lung disease that impacts a large portion of population and all ethnic groups. Driven by developments in sensor and mobile communication technology, novel Smart Asthma Management (SAM) systems have been recently established. In SAM systems, patients can create a detailed temporal event log regarding their key health indicators through easy access to a website or their smartphone. Thus, this detailed event log can be obtained inexpensively and aggregated for a large number of patients to form a centralized database for SAM systems. Taking advantage of the data available in SAM systems, we propose an individualized prognostic model based on the unique rescue inhaler usage profile of each individual patient. The model jointly combines two statistical models into a unified prognostic framework. The application of the proposed model to SAM is illustrated in this article and the effectiveness of the method is shown by both a numerical study and a case study that uses real-world data.

Suggested Citation

  • Junbo Son & Patricia Flatley Brennan & Shiyu Zhou, 2016. "Rescue inhaler usage prediction in smart asthma management systems using joint mixed effects logistic regression model," IISE Transactions, Taylor & Francis Journals, vol. 48(4), pages 333-346, April.
    • Handle: RePEc:taf:uiiexx:v:48:y:2016:i:4:p:333-346
    DOI: 10.1080/0740817X.2015.1078014
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    Cited by:

    1. Junbo Son & Yeongin Kim & Shiyu Zhou, 2022. "Alerting patients via health information system considering trust-dependent patient adherence," Information Technology and Management, Springer, vol. 23(4), pages 245-269, December.

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