This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …

Deep learning (DL) has substantially enhanced natural language processing (NLP) in
healthcare research. However, the increasing complexity of DL-based NLP necessitates …

The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …

This study presents a multimodal machine learning model to predict ICD-10 diagnostic
codes. We developed separate machine learning models that can handle data from different …

The International Classification of Diseases (ICD) is a standard for categorizing physical
conditions, which has been widely used for analyzing clinical data and monitoring health …

Background Free-text descriptions in electronic health records (EHRs) can be of interest for
clinical research and care optimization. However, free text cannot be readily interpreted by a …

In this survey, we discuss the task of automatically classifying medical documents into the
taxonomy of the International Classification of Diseases (ICD), by the use of deep neural …

Background Automatic extraction of morbid disease or conditions contained in Death
Certificates is a critical process, useful for billing, epidemiological studies and comparison …

This paper presents a new corpus of radiology medical reports written in Spanish and
labeled with ICD-10. CARES (Corpus of Anonymised Radiological Evidences in Spanish) is …

Abstract Background The International Classification of Diseases, 10th Revision (ICD-10)
has been widely used to describe the diagnosis information of patients. Automatic ICD-10 …