Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …

Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

Clinical notes are text documents that are created by clinicians for each patient encounter.
They are typically accompanied by medical codes, which describe the diagnosis and …

Word embeddings are effective intermediate representations for capturing semantic
regularities between words, when learning the representations of text sequences. We …

ICD coding is a process of assigning the International Classification of Disease diagnosis
codes to clinical/medical notes documented by health professionals (eg clinicians). This …

Medical coding is the task of assigning medical codes to clinical free-text documentation.
Healthcare professionals manually assign such codes to track patient diagnoses and …

Automated ICD coding, which assigns the International Classification of Disease codes to
patient visits, has attracted much research attention since it can save time and labor for …

Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …

Abstract The International Classification of Diseases (ICD) provides a standardized way for
classifying diseases, which endows each disease with a unique code. ICD coding aims to …

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 …