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Neural natural language processing for unstructured data in electronic health records: a review
Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
Automated medical coding on MIMIC-III and MIMIC-IV: a critical review and replicability study
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 …
Healthcare professionals manually assign such codes to track patient diagnoses and …
PLM-ICD: Automatic ICD coding with pretrained language models
Automatically classifying electronic health records (EHRs) into diagnostic codes has been
challenging to the NLP community. State-of-the-art methods treated this problem as a …
challenging to the NLP community. State-of-the-art methods treated this problem as a …
Revisiting transformer-based models for long document classification
The recent literature in text classification is biased towards short text sequences (eg,
sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents …
sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents …
Code synonyms do matter: Multiple synonyms matching network for automatic ICD coding
Automatic ICD coding is defined as assigning disease codes to electronic medical records
(EMRs). Existing methods usually apply label attention with code representations to match …
(EMRs). Existing methods usually apply label attention with code representations to match …
Knowledge injected prompt based fine-tuning for multi-label few-shot icd coding
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD
codes to a medical note with average length of 3,000+ tokens. This task is challenging due …
codes to a medical note with average length of 3,000+ tokens. This task is challenging due …
Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities
Objective: With the increasing amount and growing variety of healthcare data, multimodal
machine learning supporting integrated modeling of structured and unstructured data is an …
machine learning supporting integrated modeling of structured and unstructured data is an …
AI-based ICD coding and classification approaches using discharge summaries: A systematic literature review
The assignment of codes to free-text clinical narratives have long been recognised to be
beneficial for secondary uses such as funding, insurance claim processing and research …
beneficial for secondary uses such as funding, insurance claim processing and research …
Effective convolutional attention network for multi-label clinical document classification
Multi-label document classification (MLDC) problems can be challenging, especially for long
documents with a large label set and a long-tail distribution over labels. In this paper, we …
documents with a large label set and a long-tail distribution over labels. In this paper, we …
Automatic ICD coding via interactive shared representation networks with self-distillation mechanism
The ICD coding task aims at assigning codes of the International Classification of Diseases
in clinical notes. Since manual coding is very laborious and prone to errors, many methods …
in clinical notes. Since manual coding is very laborious and prone to errors, many methods …