@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.",
}