From Extreme Multi-label to Multi-class: A Hierarchical Approach for
Automated ICD-10 Coding Using Phrase-level Attention
- URL: http://arxiv.org/abs/2102.09136v1
- Date: Thu, 18 Feb 2021 03:19:14 GMT
- Title: From Extreme Multi-label to Multi-class: A Hierarchical Approach for
Automated ICD-10 Coding Using Phrase-level Attention
- Authors: Cansu Sen, Bingyang Ye, Javed Aslam, Amir Tahmasebi
- Abstract summary: Clinical coding is the task of assigning a set of alphanumeric codes, referred to as ICD (International Classification of Diseases), to a medical event based on the context captured in a clinical narrative.
We propose a novel approach for automatic ICD coding by reformulating the extreme multi-label problem into a simpler multi-class problem using a hierarchical solution.
- Score: 4.387302129801651
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Clinical coding is the task of assigning a set of alphanumeric codes,
referred to as ICD (International Classification of Diseases), to a medical
event based on the context captured in a clinical narrative. The latest version
of ICD, ICD-10, includes more than 70,000 codes. As this is a labor-intensive
and error-prone task, automatic ICD coding of medical reports using machine
learning has gained significant interest in the last decade. Existing
literature has modeled this problem as a multi-label task. Nevertheless, such
multi-label approach is challenging due to the extremely large label set size.
Furthermore, the interpretability of the predictions is essential for the
endusers (e.g., healthcare providers and insurance companies). In this paper,
we propose a novel approach for automatic ICD coding by reformulating the
extreme multi-label problem into a simpler multi-class problem using a
hierarchical solution. We made this approach viable through extensive data
collection to acquire phrase-level human coder annotations to supervise our
models on learning the specific relations between the input text and predicted
ICD codes. Our approach employs two independently trained networks, the
sentence tagger and the ICD classifier, stacked hierarchically to predict a
codeset for a medical report. The sentence tagger identifies focus sentences
containing a medical event or concept relevant to an ICD coding. Using a
supervised attention mechanism, the ICD classifier then assigns each focus
sentence with an ICD code. The proposed approach outperforms strong baselines
by large margins of 23% in subset accuracy, 18% in micro-F1, and 15% in
instance based F-1. With our proposed approach, interpretability is achieved
not through implicitly learned attention scores but by attributing each
prediction to a particular sentence and words selected by human coders.
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