Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach

Che-Cheng Chang; Jiann-Horng Yeh; Hou-Chang Chiu; Tzu-Chi Liu; Yen-Ming Chen; Mao-Jhen Jhou; Chi-Jie Lu

doi:10.3389/fneur.2023.1283214

Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach

Front Neurol. 2023 Dec 14:14:1283214. doi: 10.3389/fneur.2023.1283214. eCollection 2023.

Authors

Che-Cheng Chang^{1

2}, Jiann-Horng Yeh^{3

4

5}, Hou-Chang Chiu^{3

6}, Tzu-Chi Liu⁷, Yen-Ming Chen¹, Mao-Jhen Jhou⁷, Chi-Jie Lu^{7

8

9}

Affiliations

¹ Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan.
² PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan.
³ School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan.
⁴ Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei City, Taiwan.
⁵ Department of Neurology, Kaohsiung Medical University, Kaohsiung, Taiwan.
⁶ Department of Neurology, Taipei Medical University, Shuang-Ho Hospital, New Taipei City, Taiwan.
⁷ Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan.
⁸ Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan.
⁹ Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan.

Abstract

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Keywords: data mining; hospitalization; machine learning; multivariate adaptive regression splines; myasthenia gravis; prognosis.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was partially supported by the National Science and Technology Council, Taiwan (NSTC 111-2221-E-030-009), Fu Jen Catholic University (A0111181), and Fu Jen Catholic University Hospital (PL-202008004-V).