# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 2 Background
### ICU Length of Stay Prediction  
ICUs play a critical role in the healthcare system by providing specialized care to patients with severe or life-threatening conditions. One key aspect of managing ICU resources and ensuring optimal patient care is predicting the LOSs in the ICU. Accurate prediction of discharge dates improves bed hour estimation, resulting in higher average occupancy and reduced waste of hospital resources [28]. Prior studies have investigated LOS prediction in disease-specific or population-specific groups such as patients with heart failure [29] or thermal burns [30] and cardiac surgery patients [31], as well as those admitted to neonatal care units [32] or ICUs [33]. While previous work has aimed to group patients based on their medical conditions, assuming each has a predefined, recommended LOS, the LOS is influenced by multiple factors beyond medical conditions. These factors include patient characteristics, presenting complaints, complications, discharge planning, and treatment complexity, all of which can extend the original target LOS. Hence, a model capable of reliably predicting patient LOS during a single visit event could help healthcare services implement preventive measures to avoid LOS extension [34], especially in a critical and costly environment such as the ICU. Few studies have used the MIMIC-III dataset for classifying ICU patient LOSs. [PERSON] et al. [35] introduce an open-source pipeline, MIMIC-Extract, that facilitates cohort selection and pre-processing in clinical prediction tasks using the MIMIC-III dataset. The pipeline generates a versatile cohort with diverse demographic and admission coverage, enabling various prediction tasks. It employs fixed input windows and dynamic targets to prevent temporal label leakage in classification tasks such as mortality and length-of-stay prediction. Benchmark tasks are profiled using different machine learning (ML) models and demonstrate high performance, with the best-performing models reaching an accuracy of 69.5% for \"LOS > 3 days\". [PERSON] et al. [36] present an innovative method for patient-level predictions using graph-based unsupervised pre-training, where patients are represented as nodes in a graph, with edges indicating similarities in clinical features and histories. The model undergoes unsupervised pre-training on the patient population graph, enhancing its generalization capabilities without labeled outcomes. Fine-tuning for specific prediction tasks such as patient mortality or disease progression and LOS further improves the accuracy and robustness, and an evaluation on clinical datasets demonstrated significant enhancements in prediction accuracy over baseline models, with an accuracy of 71.44% for the \"LOS > 3 days\" classification task on the MIMIC-III dataset. Classification models predict the probability of a patient staying in the ICU for a specific duration, often categorized as a \"short stay\" if less than 3 days and a \"long stay\" otherwise, and the widely used metrics for evaluating the performance of a classification model are accuracy, precision, recall, F1-score, and the area under the receiver operating characteristics curve (AUROC) [37]. For classification tasks, three prominent ML methods stand out. The MLPClassifier, a neural-network-based classification algorithm, consists of interconnected layers of neurons that apply non-linear activation functions to model complex relationships between features and target variables. Trained through back propagation, it adjusts weights to minimize the difference between predicted and actual outputs, making it popular for classification tasks [38]. Support vector machines (SVMs) aim to find an optimal hyperplane that maximizes the margin between data points of different classes and handle both linear and non-linear problems through various kernel functions [39]. Random forest, an ensemble method, combines multiple decision trees to achieve accurate classification by independently predicting class labels and aggregating results through majority voting or averaging.