# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 2 Related Work
Therefore, in the context of COVID-19 diagnosis, the availability of relevant clinical variables can be challenging due to the high influx of patients in emergency rooms and the need for rapid diagnosis in pandemic situations. [PERSON] et al. [31] categorized various machine-learning-based COVID-19 prediction models into Knowledge-Driven (KD) infectious disease dynamics models and Data-Driven (DD) machine learning models. KD models, derived from all available domain knowledge, incorporate known relationships of infectious disease models, while DD models rely solely on given datasets without domain knowledge. KD models typically use a set of mathematical equations, like ordinary differential equations, including physically interpretable parameters to reveal the key characteristics and transmission rules of infectious diseases. However, they may suffer from poor predictive power due to the simplified or ambiguous explanations of mechanistic processes and parameter uncertainty. DD models employ various machine learning techniques, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN), to predict COVID-19 mortality risk. These models can handle unknown non-linear relationships in infectious disease transmission mechanisms through data learning but risk reflecting model bias in their predictions due to the bias-variance tradeoff. Ensemble learning, which combines the predictive abilities of two or more base learner models to reduce bias and variance, thereby improving overall prediction performance, has seen recent application in analyzing COVID-19 data. However, determining the optimal combination of models for optimal performance remains challenging, with most researchers manually constructing stacking ensemble models to predict COVID-19's clinical severity [32; 33; 34; 35]. Additionally, some studies opted for simplified severity classificationsrelated only to survival and death, rather than attempting to classify a diverse range of clinical severities [36; 37]. To address the challenge of automating machine learning model construction, current Automated Machine Learning (AutoML) technology automatically generates prediction pipelines in a data-driven manner, producing high-quality predictions. AutoML reduces repetitive tasks to the data analysis and enables the development of applications without the need for specific expertise in machine learning or statistics. For example, [PERSON] et al. [38] used the open-source H2O.ai AutoML package to predict COVID-19 patient mortality, and [PERSON] et al. [39] utilized the PyCaret library to select algorithms for identifying COVID-19 patients at risk of death or needing hospitalization. However, most existing AutoML research focuses on finding the optimal model among single classifiers or is limited by the use of a restricted range of single classifiers, failing to ensure model diversity and often not optimizing data and feature preprocessors, focusing solely on model optimization. Although some AutoML approaches include stacking ensemble techniques, they generally have a simple single-layer structure, which limits the integration needed to address complex data structures and diverse prediction requirements. The recent advancement of Large Language Models (LLMs) has introduced new possibilities for processing biomedical data through Natural Language Processing (NLP), including identifying COVID-19 infections. [PERSON] et al. [40] proposed a system that automatically predicts whether patients' chest CT scan reports match radiological findings indicative of COVID-19, using BiLSTM-, CNN-, and ANN-based text classification techniques. [PERSON] et al. [41] developed an NLP model that accurately classifies EHRs and identifies COVID-19 cases using BioClinicalBERT and DistilBERT, potentially reducing clinician response time and improving access to antiviral treatments. [PERSON] et al.