@inproceedings{amoia-etal-2018-scalable,
title = "Scalable Wide and Deep Learning for Computer Assisted Coding",
author = "Amoia, Marilisa and
Diehl, Frank and
Gimenez, Jesus and
Pinto, Joel and
Schumann, Raphael and
Stemmer, Fabian and
Vozila, Paul and
Zhang, Yi",
editor = "Bangalore, Srinivas and
Chu-Carroll, Jennifer and
Li, Yunyao",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
month = jun,
year = "2018",
address = "New Orleans - Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-3001",
doi = "10.18653/v1/N18-3001",
pages = "1--7",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Scalable Wide and Deep Learning for Computer Assisted Coding
%A Amoia, Marilisa
%A Diehl, Frank
%A Gimenez, Jesus
%A Pinto, Joel
%A Schumann, Raphael
%A Stemmer, Fabian
%A Vozila, Paul
%A Zhang, Yi
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F amoia-etal-2018-scalable
%X 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.
%R 10.18653/v1/N18-3001
%U https://aclanthology.org/N18-3001
%U https://doi.org/10.18653/v1/N18-3001
%P 1-7
Markdown (Informal)
[Scalable Wide and Deep Learning for Computer Assisted Coding](https://aclanthology.org/N18-3001) (Amoia et al., NAACL 2018)
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.