# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 1 Introduction  
The length of stay (LOS) for a hospitalized patient refers to the duration of their single admission measured in days. Besides serving as a key metric for assessing hospital resource utilization and the operational efficiency of healthcare systems, the LOS offers valuable insights into patient flow within care units [1]. In recent decades, hospitals worldwide have seen a notable decline in the average LOS, with the United States experiencing a reduction from approximately 20.5 days in 1960 to merely 6.5 days in 2021, making it among the shortest LOS globally [2]. However, despite this reduction, the cost of inpatient care remains exceedingly high. For instance, the average total ICU cost in the United States was USD 13,443 in 2021 [3]. While reducing the LOS may not always be feasible as a cost-saving measure, hospitals can still make savings by optimizing the allocation of personnel and resources. Accurately predicting the LOS can facilitate this optimization and serves as an effective strategy for healthcare services to implement preventative measures aimed at avoiding LOS extensions [1]. This is beneficial from different aspects, including the patient's condition and medical plan, family, the hospital, and the insurance company [4]. Additionally, the averageexpenses associated with patients who experience a prolonged stay in the ICU are seven times higher compared to those who have a regular LOS [5], with a prolonged LOS being more than a week. Comorbidities, patient-specific factors such as age and frailty, and logistical challenges like delayed admissions and bed shortages all contribute to prolonged stays [6; 7]. Furthermore, in the ICU, healthcare providers face the challenge of dealing with a huge amount of information and cognitive demands, leading to potential information overload and difficulties with processing critical patient details, which increases the risk of missed diagnoses and medical errors, impacting patient outcomes significantly [8]. To address these issues, our proposed solution is to automatically generate diagnoses and problems from progress notes, which, along with other clinical and demographic features, will predict ICU patients' LOSs. This automation helps reduce information overload and cognitive biases that may arise during the interpretation of complex patient data, aiding with understanding patient conditions accurately. In addition to that, by accurately predicting ICU patients' LOSs, resource allocation and management can be optimized through proactive planning of patient transfers and discharges. Accordingly, our approach begins by tackling a challenge proposed by Physionet (PhysioNet: _The Research Resource for Complex Physiologic Signals_, 2024, [[https://physionet.org](https://physionet.org)]([https://physionet.org](https://physionet.org)) (accessed 10 June 2024)) (\"BioNLP Workshop 2023 Shared Task 1A: Problem List Summarization\", 2023, [[https://physionet.org/content/bionlp-workshop-2023-task-1a/2.0.0](https://physionet.org/content/bionlp-workshop-2023-task-1a/2.0.0)]([https://physionet.org/content/bionlp-workshop-2023-task-1a/2.0.0](https://physionet.org/content/bionlp-workshop-2023-task-1a/2.0.0)) (accessed 10 June 2024)), which is a renowned platform supporting physiological signal research that is managed by MIT's Computational Physiology laboratory and backed by the National Institute of Biomedical Imaging and Bioengineering [9]. The challenge is entitled: \"BioNLP Workshop 2023: Problem List Summarization\" [10], and the main task is to generate a list of diagnoses and problems in a patient's daily care plan using input from the provider's progress notes during hospitalization in the ICU. The method then focuses on the extraction of additional features from the dataset that will contribute to a comprehensive understanding of each patient's medical profile. Finally, the generated problems and diagnoses and the extracted variables are used as features to predict the patient's LOS in the ICU.