Scalable Wide and Deep Learning for Computer Assisted Coding

Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joel Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, Yi Zhang


Abstract
In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. As a first step towards reimbursement healthcare institutions need to associate ICD-10 billing codes to these documents. This is done by trained clinical coders who may use a computer assisted solution for shortlisting of codes. In this work, we present our work to build a machine learning based scalable system for predicting ICD-10 codes from electronic medical records. We address data imbalance issues by implementing two system architectures using convolutional neural networks and logistic regression models. We illustrate the pros and cons of those system designs and show that the best performance can be achieved by leveraging the advantages of both using a system combination approach.
Anthology ID:
N18-3001
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
Month:
June
Year:
2018
Address:
New Orleans - Louisiana
Editors:
Srinivas Bangalore, Jennifer Chu-Carroll, Yunyao Li
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://aclanthology.org/N18-3001
DOI:
10.18653/v1/N18-3001
Bibkey:
Cite (ACL):
Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joel Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, and Yi Zhang. 2018. Scalable Wide and Deep Learning for Computer Assisted Coding. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 1–7, New Orleans - Louisiana. Association for Computational Linguistics.
Cite (Informal):
Scalable Wide and Deep Learning for Computer Assisted Coding (Amoia et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-3001.pdf
Video:
 https://aclanthology.org/N18-3001.mp4