Frank Diehl


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Large Scale Sequence-to-Sequence Models for Clinical Note Generation from Patient-Doctor Conversations
Gagandeep Singh | Yue Pan | Jesus Andres-Ferrer | Miguel Del-Agua | Frank Diehl | Joel Pinto | Paul Vozila
Proceedings of the 5th Clinical Natural Language Processing Workshop

We present our work on building large scale sequence-to-sequence models for generating clinical note from patient-doctor conversation. This is formulated as an abstractive summarization task for which we use encoder-decoder transformer model with pointer-generator. We discuss various modeling enhancements to this baseline model which include using subword and multiword tokenization scheme, prefixing the targets with a chain-of-clinical-facts, and training with contrastive loss that is defined over various candidate summaries. We also use flash attention during training and query chunked attention during inference to be able to process long input and output sequences and to improve computational efficiency. Experiments are conducted on a dataset containing about 900K encounters from around 1800 healthcare providers covering 27 specialties. The results are broken down into primary care and non-primary care specialties. Consistent accuracy improvements are observed across both of these categories.


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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.


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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.


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The COST 278 MASPER Initiative - Crosslingual Speech Recognition with Large Telephone Databases
Andrej Žgank | Zdravko Kačič | Frank Diehl | Klara Vicsi | Gyorgy Szaszak | Jozef Juhar | Slavomir Lihan
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)


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SPEECON – Speech Databases for Consumer Devices: Database Specification and Validation
Dorota Iskra | Beate Grosskopf | Krzysztof Marasek | Henk van den Heuvel | Frank Diehl | Andreas Kiessling
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)