Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation

by   Hang Dong, et al.

Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to improve the efficiency and accuracy of medical coding. Recent studies on deep learning for automated medical coding achieved promising performances. However, the explainability of these models is usually poor, preventing them to be used confidently in supporting clinical practice. Another limitation is that these models mostly assume independence among labels, ignoring the complex correlation among medical codes which can potentially be exploited to improve the performance. We propose a Hierarchical Label-wise Attention Network (HLAN), which aimed to interpret the model by quantifying importance (as attention weights) of words and sentences related to each of the labels. Secondly, we propose to enhance the major deep learning models with a label embedding (LE) initialisation approach, which learns a dense, continuous vector representation and then injects the representation into the final layers and the label-wise attention layers in the models. We evaluated the methods using three settings on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS COVID-19 shielding codes. Experiments were conducted to compare HLAN and LE initialisation to the state-of-the-art neural network based methods. HLAN achieved the best Micro-level AUC and F_1 on the top-50 code prediction and comparable results on the NHS COVID-19 shielding code prediction to other models. By highlighting the most salient words and sentences for each label, HLAN showed more meaningful and comprehensive model interpretation compared to its downgraded baselines and the CNN-based models. LE initialisation consistently boosted most deep learning models for automated medical coding.


page 1

page 2

page 3

page 4


Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding

International Classification of Diseases (ICD) coding plays an important...

Medical Codes Prediction from Clinical Notes: From Human Coders to Machines

Prediction of medical codes from clinical notes is a practical and essen...

Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study

Unsupervised pretraining is an integral part of many natural language pr...

Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?

The medical codes prediction problem from clinical notes has received su...

Description-based Label Attention Classifier for Explainable ICD-9 Classification

ICD-9 coding is a relevant clinical billing task, where unstructured tex...

Modeling Diagnostic Label Correlation for Automatic ICD Coding

Given the clinical notes written in electronic health records (EHRs), it...

Automated Clinical Coding: What, Why, and Where We Are?

Clinical coding is the task of transforming medical information in a pat...

Please sign up or login with your details

Forgot password? Click here to reset