# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
Stacking ensemble models typically face challenges such as computational complexity and an extensive computation time. Although our processes, such as data preprocessing, feature selection, and oversampling, are automated using our proposed algorithms, they still require significant computational resources. Consequently, rapidly generating these models remains challenging in time-sensitive tasks and during data updates. Furthermore, although our implementation outperformed AutoML approaches in experimental evaluations, we were unable to enhance computational efficiency compared to AutoML approaches, which often utilize cloud computing or parallel processing. Instead, our goal was to enhance the performance in the identification of various COVID-19 severity levels within the constraints of clinical environments. Moreover, since our research was based on data from South Korean patients, the results may not be generalizable to other ethnic groups, such as Caucasians or Middle Eastern Asians. Therefore, external validation using a more diverse population is necessary to evaluate the effectiveness of the model across different ethnic groups. Additionally, our model's performance needs to be verified in real-world scenarios. Our research was limited to clinical epidemiological datasets. In future work, we aim to apply our technique to diverse datasets, including those from various ethnic groups. To achieve this, we plan to develop a user-friendly web application. This application will enable a real-time training framework that allows our model to learn from prospectively collected data and provides optimizations to reduce computational overhead via the use of cloud computing resources. Furthermore, the surge in COVID-19 cases has led to a significant increase in medical documents, such as clinical notes, offering new opportunities for our approach to identify patient severity through NLP and LLMs. However, LLMs present their own challenges, including the risk of generating hallucinated content and a dependency on prompt engineering for quality results. We intend to address these challenges and incorporate LLMs into our technique. This will automate the interpretation of text-based unstructured data. This integration could significantly enhance the strengths of our data-driven strategy. Furthermore, we will examine the model's adaptability and relevance in the post-COVID period, aiming to enhance its practical implementation. Our goal is not only to refine the technical aspects of our approach but also to ensure its practical applicability in real-world healthcare settings. Conceptualization: G.-W.K., H.S. and D.-H.L. ; Methodology: G.-W.K. ; Software: G.-W.K. and C.-Y.J. ; Validation: G.-W.K., H.S. and D.-H.L. ; Formal Analysis: G.-W.K., H.S. and C.-Y.J. ; Investigation: G.-W.K. ; Resources: C.-Y.J., H.S. and D.-H.L. ; Data Curation: G.-W.K., C.-Y.J. and H.S. ; Writing--Original Draft Preparation: G.-W.K. ; Writing--Review and Editing: G.-W.K., C.-Y.J. and D.-H.L. ; Visualization: G.-W.K. and C.-Y.J. ; Supervision: D.-H.L. ; Project Administration: G.-W.K. and D.-H.L. ; Funding Acquisition: G.-W.K. All authors have read and agreed to the published version of the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (NRF-2021 RIG1A1006381). The protocol of this retrospective study was approved by the Institutional Review Board of Korea University Ansan Hospital (K2021-0013-005) and followed the principles of the Declaration of Helsinki. The requirement for obtaining informed patient consent was waived by the institutional review board (K2021-0013-005) due to the retrospective nature of the study. The data supporting the findings of this paper were provided by the KCDC, but their availability is subject to certain restrictions. As these data were used under a license for this specific study, they are not publicly accessible.