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 …
Automated clinical coding: what, why, and where we are?
Clinical coding is the task of transforming medical information in a patient's health records
into structured codes so that they can be used for statistical analysis. This is a cognitive and …
into structured codes so that they can be used for statistical analysis. This is a cognitive and …
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 …
[HTML][HTML] Knowledge injected prompt based fine-tuning for multi-label few-shot icd coding
Abstract 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 …
multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is …
[HTML][HTML] Does the magic of BERT apply to medical code assignment? A quantitative study
S Ji, M Hölttä, P Marttinen - Computers in biology and medicine, 2021 - Elsevier
Unsupervised pretraining is an integral part of many natural language processing systems,
and transfer learning with language models has achieved remarkable results in downstream …
and transfer learning with language models has achieved remarkable results in downstream …
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 …
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 …
[HTML][HTML] Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation
Background Diagnostic or procedural coding of clinical notes aims to derive a coded
summary of disease-related information about patients. Such coding is usually done …
summary of disease-related information about patients. Such coding is usually done …
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 …