# Using Enhanced Representations to Predict Medical Procedures from Clinician Notes
## 1 Introduction  
Critical reasoning is one of the most valuable skills for a healthcare professional when faced with a new medical case. A comprehensive view of the patient's history allows for a better assessment of their condition. Thus, having quick access to all relevant information about a patient promotes better, faster, and more cost-effective decision-making [1]. This idea is reflected in the upward trend in Electronic Health Records (EHR) adoption worldwide in recent years, which seeks to increase efficiency and improve quality of care by giving care providers access to comprehensive and accurate patient records [2]. However, as vast amounts of patient data become available, there is a growing motivation in the community to develop strategies to optimize the use of this type of information. Currently, an essential source of information usually included in EHRs corresponds to unstructured data in the form of clinical texts acquired during medical checks and hospital admissions [3]. These annotations contain valuable information about each patient, but making practical use of this information is not trivial, due to the peculiarities of the texts, such as the presence of misspellings, typos, and ambiguous terms [4]. To deal with this lack of structure, clinicians assign specific medical codes to known conditions and events from their observations. To this end, medical professionals use various coding standards for different applications. Healthcare Common Procedure Coding System (HCPCS) [5], Current Procedural Terminology (CPT) [6], and Australian Classification of Health Interventions (ACHI) [7], for example, provide a collection of standardized codes for medical procedures, supplies, services, and interventions. One of the most used standards is the International Classification of Disease (ICD) [8], developed and maintained by the World Health Organization (WHO) and recognized as one of the main bases for comparing health statistics related to morbidities and causes of death. The standard is periodically updated to reflect advances in medical knowledge and involves healthcare professionals worldwide [9]. In 2024, the standard spans 11 revisions,the latter of which, ICD-11, introduces a computable knowledge framework consisting of a large ontology of around 80,000 medical entities, as presented by [PERSON] et al. [8]. Each entity describes a health-related phenomenon and establishes relationships with other entities defined in the standard. Given its wide adoption, this study is built around the ICD standard. To evaluate our results with comparable approaches to ICD procedure prediction in the literature [10; 11], we used the Medical Information Mart for Intensive Care III (MIMIC-III) database [12], which is based on the ninth revision of the standard (ICD-9). The ICD-9 revision comprises a taxonomy of more than 15,000 codes for disease diagnoses and procedures. Manually assigning medical codes still requires a medical expert with good knowledge to make the associations. This task is time-consuming and, most importantly, error-prone, particularly due to the large size of the ICD taxonomy [10] and the hindsight bias that can lead to incomplete or downright wrong clinical evaluations [13]. This coding standard is also widely used to assess medical costs and support billing processes with insurance companies, and consequently improper usage can have monetary consequences for institutions [14]. To mitigate these limitations, automatic ICD coding has been explored for decades using traditional Machine Learning (ML) tools [15; 16; 17] and, more recently, incorporating Deep Learning (DL) elements into the mix [3; 10; 18; 19; 20]. For an extended overview of coding schemes, relevant databases, and State-of-the-Art (SOTA) solutions for automatic ICD coding, the interested reader will find a wealth of information in the extensive review by [PERSON] et al. [21].