# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 2 Background
### Clinical Text Summarization
#### 2.1.4 Existing Approaches for Clinical Text Summarization  
Electronic health records (EHRs) serve as a comprehensive and ongoing record of a patient's health information. EHR daily progress notes are written by the healthcare providers in order to track the ongoing progress of the patient on a daily basis; they serve as a chronological record of the patient's health status and play a crucial role in facilitating communication among healthcare providers and monitoring the patient's progress throughout their healthcare journey [17]. EHR daily progress notes typically follow the SOAP structure, which stands for subjective, objective, assessment, and plan. It is a documentation method designed by [PERSON] [18] to present patient's problems in a highly structured way. Within each section, there are multiple components that focus on different aspects of the patient's case and present relevant information. The _Subjective_ section records subjective information obtained from the patient or caregiver, such as chief complaints, symptoms, and relevant medical history. The _Objective_ section contains measurable data like vital signs, examination findings, and test results. The _Assessment_ section summarizes the healthcare provider's diagnosis, evaluation of symptoms, and overall clinical impressions. Finally, the _Plan_ outlines the proposed treatment plan, including medications, procedures, referrals, follow-up appointments, and patient education [19]. Recent research on EHR text summarization can be classified by the approach used. For the extractive summarization techniques, [PERSON] et al. [20] propose a disease-specific summarization task that extracts sentences from progress notes, focusing primarily on progress notes by physicians and nurse practitioners; the pipeline includes a basic natural language processing (NLP) layer along with additional EHR-specific components such as note section classification, disease context identification, and adverse drug event detection. HARVEST [21], an EHR summarizer deployed in a New York hospital, incorporates a Markov chain named-entity tagger to identify diseases that are explicitly mentioned in clinical notes as well as a TF-IDF scorer, which assesses the importance of these disease mentions by considering their frequency in the document and their rarity across the entire dataset. For the abstractive summarization techniques, several works focus on summarizing radiology reports into an impression, which is a short piece of text that states the findings from the source image [22; 23]. [PERSON] and [PERSON] [24] present a technique for generating snippets, which are concise summaries extracted from aligned sentences. The snippets are designed to capture the essential information from the clinic visit and provide a condensed representation for easier reference and understanding. [PERSON] et al. [19] introduce a novel approach to automatically summarize patients' medical problems from hospital progress notes. Their experiments involved fine-tuning two pre-trained sequence-to-sequence models: T5 and BART. The study demonstrates promising results, highlighting the potential of fine-tuning the pre-trained T5 model for accurate and efficient problem summarization in the healthcare domain. It also discusses future research directions to improve the generalizability of the approach and improve the quality of generated summaries, and it proposes a manually annotated dataset based on a subset of the large and publicly available MIMIC-III database [25]. Based on these findings, the BioNLP Workshop 2023 launched a shared task on problem list summarization in January 2023 [26]. This task aimed to foster research on building NLP models for real-world diagnostic decision support in order to enhance healthcare providers' decision-making and patient care quality. Participants were tasked with developing models to generate lists of diagnoses and problems from the EHR daily progress notes of critically ill patients. Eight teams submitted their systems for evaluation, including us.