Fabian Stemmer


2020

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Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models
Seppo Enarvi | Marilisa Amoia | Miguel Del-Agua Teba | Brian Delaney | Frank Diehl | Stefan Hahn | Kristina Harris | Liam McGrath | Yue Pan | Joel Pinto | Luca Rubini | Miguel Ruiz | Gagandeep Singh | Fabian Stemmer | Weiyi Sun | Paul Vozila | Thomas Lin | Ranjani Ramamurthy
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations

We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach. We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequence-to-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale.

2018

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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
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

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.