A Deep Learning Model for the Analysis of Medical Reports in ICD-10 Clinical Coding Task
Authors: Marco Polignano, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro (Università di Bari Aldo Moro, 2020)
Topic: In-domain IR and IE

Abstract
The practice of assigning a uniquely identifiable and easily traceable code to pathology from medical diagnoses is an added value to the current modality of archiving health data collected to build the clinical history of each of us. Unfortunately, the enormous amount of possible pathologies and medical conditions has led to the realization of extremely wide international codifications that are difficult to consult even for a human being. This difficulty makes the practice of annotation of diagnoses with ICD-10 codes very cumbersome and rarely performed. In order to support this operation, a classification model was proposed, able to analyze medical diagnoses written in natural language and automatically assign one or more international reference codes. The model has been evaluated on a dataset released in the Spanish language for the eHealth challenge (CodiEsp) of the international conference CLEF 2020, but it could be extended to any language with latin characters. We proposed a model based on a two-step classification process based on BERT and BiLSTM. Although still far from an accuracy sufficient to do without a licensed physician opinion, the results obtained show the feasibility of the task and are a starting point for future studies in this direction.

Introduction
In many of the existing systems for storing patient clinical data, the medical report of the doctor is stored in the form of textual data. Only in a few cases, one or more identification codes are assigned to each of the diagnosed conditions. The process of assigning a unique code to pathologies, symptoms, clinical situations, and drugs is commonly referred to as Clinical Coding. Among the most widely used coding systems, we can find the tenth version of the international medical glossary published by WHO (World Health Organization), commonly known as ICD-10. It contains codes for diseases, signs, and symptoms, abnormal results, complaints, social situations, and external causes of injury or illness. The United States uses its national variant of the ICD-10 called the ICD-10 Clinical Modification (ICD-10-CM). A procedure classification called the ICD-10 Procedure Coding System (ICD-10-PCS) has also been developed for the acquisition of hospitalization procedures. There are over 70,000 ICD-10-PCS procedure codes and over 69,000 ICD-10-CM diagnosis codes, compared to about 3,800 procedure codes and about 14,000 diagnosis codes in the previous ICD-9-CM. The use of an international classification to annotate medical diagnoses makes the health system interoperable between different countries. Among the many possibilities of using ICD codes, a doctor of any nationality could thus be able to read, analyze, and use the medical history of a patient even if of a different nationality. In addition, diagnostic patterns used by clinicians could be identified to improve automatic disease prediction strategies and provide automatic specialist support for decision making. These observations strongly support the need for automated systems to support clinicians to perform this task quickly and without human intervention. The contribution of our work is a novel model for ICD-10 coses annotation We proposed a model based on Bi-LSTM and BERT to assign one or more ICD-10 codes to the medical diagnosis. Specifically, we have designed our approach as a two-step process. In the first one, we use a BERT-based classifier to select from the dataset only a subset of sentences that could be candidates for the annotation step. In particular, those phrases are them that could e annotated with one or more codes. The phrases left were generally speaking expressions. In the second step, we used a Bi-LSTM model to analyze the candidate sentences and assign one or more codes to them. The results are encouraging and a good starting point for further investigations. The rest of the paper is structured as follows. We start analyzing related works, and we go through the description of the model and the dates. Finally, we analyze the results obtained, and we expose our consideration of the task in the conclusion section.

Conclusion
The ability to automatically annotate medical reports with international codes is an open and relevant research challenge for future technologies of global medical data sharing. In this work, we have proposed a model based on the state of the art technologies to support the doctors during this time-consuming task. For example we can imagine the use of our model in a system that can suggest to the doctor a set of possible codes to choose from to note down the medical diagnosis. Such a suggestion could make this annotation process less complex for any human annotator. It is important to keep in mind that the ICD-10 code contains more than 70,000 codes and their number is constantly increasing. During the competition it was shown that the large number of possible labels was also the most difficult problem to deal with even for automatic systems. In this regard, in fact, many systems based on machine learning have found many more difficulties than hybrid systems able to select through linguistic rules a subset of possible assignable codes. This observation will be used by us in the future to improve the model proposed here. Currently, the results obtained are encouraging and confirm the possibility of tackling it with current technologies. Nevertheless, our system cannot obtain a score of reliability, such as acting independently, and the opinion of a human coder is still essential. The system proposed here has not won the challenge at CLEF 2020 but is still a good starting point for further studies that want to use those technologies to resolve the clinical coding task.