Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study

Yao Tong, Beilei Lin, Gang Chen and Zhenxiang Zhang
Additional contact information
Yao Tong: School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China
Beilei Lin: School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China
Gang Chen: Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
Zhenxiang Zhang: School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China

IJERPH, 2022, vol. 19, issue 3, 1-18

Abstract: Continuity of care (COC) has been shown to possess numerous health benefits for chronic diseases. Specifically, the establishment of its level can facilitate clinical decision-making and enhanced allocation of healthcare resources. However, the use of a generalizable predictive methodology to determine the COC in patients has been underinvestigated. To fill this research gap, this study aimed to develop a machine learning model to predict the future COC of asthma patients and explore the associated factors. We included 31,724 adult outpatients with asthma who received care from the University of Washington Medicine between 2011 and 2018, and examined 138 features to build the machine learning model. Following the 10-fold cross-validations, the proposed model yielded an accuracy of 88.20%, an average area under the receiver operating characteristic curve of 0.96, and an average F1 score of 0.86. Further analysis revealed that the severity of asthma, comorbidities, insurance, and age were highly correlated with the COC of patients with asthma. This study used predictive methods to obtain the COC of patients, and our excellent modeling strategy achieved high performance. After further optimization, the model could facilitate future clinical decisions, hospital management, and improve outcomes.

Keywords: continuity of care; asthma; predicting; feature engineering; machine learning; retrospective study (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/3/1237/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/3/1237/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:3:p:1237-:d:731057

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().