## 1 Introduction
As a result, the healthcare system is under a heavy burden, which may affect the criteria for ICU admission, the interventions used, and the duration, all of which may affect the patient's prognosis [10]. The harnessing of big data for clinical and basic research analyses and applications to improve human well-being and health, such as the combination of big data and artificial intelligence, can assist physicians in diagnosing and treating diseases and improving the quality of care. Predictive models have been developed over the last few decades as important risk-assessment tools and are utilized in a variety of healthcare settings [11]. While traditional research in intensive-care settings has predominantly focused on quantitative variables to predict patient mortality, the utility of qualitative data is increasingly acknowledged. Current statistical models are primarily oriented towards manipulating quantitative data; however, significant challenges remain in standardizing and effectively utilizing qualitative data [12]. A wealth of patient information, particularly social and behavioral factors such as social isolation, stress, and psychological complexity, is stored in the unstructured data fields of the Electronic Health Records (EHRs). Recent advancements in artificial intelligence, especially through enhanced natural language-processing systems, have proven effective at extracting symptom information and differentiating between observed symptoms and those negated within EHR narratives [13; 14]. The integration of AI into clinical medicine has broadened to include applications such as preclinical data processing, point-of-care diagnostic assistance, patient stratification, treatment decision-making, and early warning systems for primary and secondary prevention, reflecting a comprehensive approach to patient care. This capability not only enhances the accuracy and robustness of predictive algorithms but also underscores the importance of integrating both clinical and non-clinical factors related to patients' everyday lives in predicting outcomes [15; 16]. However, processing the unstructured data that constitute medical big data takes a lot of time and money. This is especially true for the numerical portion, which is often an important component, presented in the bulk of clinical notes during treatment and hospitalization. By taking unstructured data as input, we are not using raw physiological data, but the perception and judgment of medical and nursing professionals in free-text annotations. These allow us to access higher-level concepts that are not present in physiological data. [PERSON] et al. used the BERTopic to evaluate, alongside other topic modeling algorithms, to determine its effectiveness in analyzing Twitter data related to travel and the COVID-19 pandemic. Based on the results of the evaluation, BERTopic was found to be one of the most effective algorithms for this type of analysis [17]. [PERSON] et al. proposed a machine learning-based approach to improve the classification of cognitive distortions in Arabic content over Twitter. The proposed approach enriches text representation by defining the latent topics within tweets using a transformer-based topic-modeling technique called BERTopic [18]. Both BERTopic and Latent Dirichlet Allocation (LDA) are topic-modeling algorithms that can convert text data into topic vectors. However, BERTopic uses the BERT model, while LDA uses a probabilistic model [19]. Modeling LDA for free text topics has some shortcomings. For instance, when LDA processes long texts, it splits them into multiple short texts, leading to information loss and dispersion. LDA only considers the frequency of word occurrences and lacks an understanding of contextual relationships among words. To use LDA, one needs to perform multiple cross-validation and parameter adjustments to obtain better results. Overall, BERTopic outperforms LDA in topic modeling, especially in long text and contextual understanding [20].