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 …
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 …
A unified review of deep learning for automated medical coding
Automated medical coding, an essential task for healthcare operation and delivery, makes
unstructured data manageable by predicting medical codes from clinical documents. Recent …
unstructured data manageable by predicting medical codes from clinical documents. Recent …
[PDF][PDF] A Novel Metadata Based Multi-Label Document Classification Technique.
From the beginning, the process of research and its publication is an ever-growing
phenomenon and with the emergence of web technologies, its growth rate is overwhelming …
phenomenon and with the emergence of web technologies, its growth rate is overwhelming …
Automating the overburdened clinical coding system: challenges and next steps
Artificial intelligence (AI) and natural language processing (NLP) have found a highly
promising application in automated clinical coding (ACC), an innovation that will have …
promising application in automated clinical coding (ACC), an innovation that will have …
Multi-Label few-shot ICD coding as autoregressive generation with prompt
Abstract Automatic International Classification of Diseases (ICD) coding aims to assign
multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is …
multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is …
Towards semi-structured automatic ICD coding via tree-based contrastive learning
Abstract Automatic coding of International Classification of Diseases (ICD) is a multi-label
text categorization task that involves extracting disease or procedure codes from clinical …
text categorization task that involves extracting disease or procedure codes from clinical …
A label distribution manifold learning algorithm
In this paper, we propose a novel label distribution manifold learning (LDML) method for
solving the multilabel distribution learning problem. First, using manifold learning, we extract …
solving the multilabel distribution learning problem. First, using manifold learning, we extract …
Knowledge-based dynamic prompt learning for multi-label disease diagnosis
Pretrained language models (PLMs) have been developed rapidly which establish
impressive performance on many open-domain downstream tasks. However, conducting …
impressive performance on many open-domain downstream tasks. However, conducting …