Multitask Recalibrated Aggregation Network for Medical Code Prediction

04/02/2021
by   Wei Sun, et al.
25

Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/06/2021

Multi-task Balanced and Recalibrated Network for Medical Code Prediction

Human coders assign standardized medical codes to clinical documents gen...
research
01/08/2022

A Unified Review of Deep Learning for Automated Medical Coding

Automated medical coding, an essential task for healthcare operation and...
research
12/23/2020

Entropic Measures of Complexity in a New Medical Coding System

Background: Transitioning from an old medical coding system to a new one...
research
03/04/2022

AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment

Given a deep learning model trained on data from a source site, how to d...
research
09/29/2020

Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment

Manual annotation of ICD-9 codes is a time consuming and error-prone pro...
research
07/07/2023

MDACE: MIMIC Documents Annotated with Code Evidence

We introduce a dataset for evidence/rationale extraction on an extreme m...

Please sign up or login with your details

Forgot password? Click here to reset