Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Sep 24, 2023
Date Accepted: Nov 24, 2023
Risk Prediction of Emergency Department Visits in Lung Cancer Patients Utilizing Machine Learning
ABSTRACT
Background:
Lung cancer patients are one of the most frequent visitors to emergency departments due to cancer-related problems, and the prognosis for lung cancer patients who seek emergency care is dismal. Given that lung cancer patients frequently visit healthcare facilities for treatment or follow-up, the ability to predict emergency department visits based on clinical information gleaned from their routine visits would enhance hospital resource utilization and patient outcomes.
Objective:
This study proposed a machine learning-based prediction model to identify risk factors for emergency department visits by lung cancer patients.
Methods:
This was a retrospective observational study among lung cancer patients diagnosed at Seoul National University Bundang Hospital, a tertiary general hospital in South Korea, between January 2010 and December 2017. The primary outcome was an emergency department visit within 30 days of an outpatient visit. This study developed a machine learning-based prediction model using a common data model. In addition, the importance of features that influenced the decision-making of the model output was analyzed to identify significant clinical factors.
Results:
The best performance model demonstrated an area under the receiver operating characteristic curve of 0.73 in its ability to predict the attendance of lung cancer patients in emergency departments. The frequency of recent visits to the emergency department and several laboratory test results that are typically collected during cancer treatment follow-up were revealed as influencing factors for the model output.
Conclusions:
This study developed a machine learning-based risk prediction model using the common data model and identified influencing factors for emergency department visits by lung cancer patients. The predictive model contributes to the efficiency of resource utilization and healthcare service quality by facilitating the identification and early intervention of high-risk patients. This study demonstrated the possibility of collaborative research among different institutions using the common data model for precision medicine in lung cancer.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.