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

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

Che-Cheng Chang  1   2 , Jiann-Horng Yeh  3   4   5 , Hou-Chang Chiu  3   6 , Tzu-Chi Liu  7 , Yen-Ming Chen  1 , Mao-Jhen Jhou  7 , Chi-Jie Lu  7   8   9
Affiliations

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

Che-Cheng Chang et al. Front Neurol. .

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.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer CH declared a shared affiliation, with no collaboration with the authors to the handling editor at the time of the review. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Overall flowchart of the participant enrollment process.
Figure 2
Figure 2
Data preprocessing processes for training and testing the MARS model.
Figure 3
Figure 3
Influence of important variables on the average number of hospital days. (A) Age at admission; (B) disease duration; (C) MGFA clinical classification; (D) PSL maximum daily dose.
See this image and copyright information in PMC

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