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    <abstract>The goal of this work is to utilize Electronic Medical Record (EMR) data for real-time Clinical Decision Support (CDS). We present a deep learning approach to combining in real time available diagnosis codes (ICD codes) and free-text notes: Patient Context Vectors. Patient Context Vectors are created by averaging ICD code embeddings, and by predicting the same from free-text notes via a Convolutional Neural Network. The Patient Context Vectors were then simply appended to available structured data (vital signs and lab results) to build prediction models for a specific condition. Experiments on predicting ARDS, a rare and complex condition, demonstrate the utility of Patient Context Vectors as a means of summarizing the patient history and overall condition, and improve significantly the prediction model results.</abstract>
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%0 Conference Proceedings
%T Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support
%A Apostolova, Emilia
%A Wang, Tony
%A Tschampel, Tim
%A Koutroulis, Ioannis
%A Velez, Tom
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F apostolova-etal-2019-combining
%X The goal of this work is to utilize Electronic Medical Record (EMR) data for real-time Clinical Decision Support (CDS). We present a deep learning approach to combining in real time available diagnosis codes (ICD codes) and free-text notes: Patient Context Vectors. Patient Context Vectors are created by averaging ICD code embeddings, and by predicting the same from free-text notes via a Convolutional Neural Network. The Patient Context Vectors were then simply appended to available structured data (vital signs and lab results) to build prediction models for a specific condition. Experiments on predicting ARDS, a rare and complex condition, demonstrate the utility of Patient Context Vectors as a means of summarizing the patient history and overall condition, and improve significantly the prediction model results.
%R 10.18653/v1/W19-5007
%U https://aclanthology.org/W19-5007/
%U https://doi.org/10.18653/v1/W19-5007
%P 66-70

Markdown (Informal)

[Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support](https://aclanthology.org/W19-5007/) (Apostolova et al., BioNLP 2019)

• Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support (Apostolova et al., BioNLP 2019)

ACL

• Emilia Apostolova, Tony Wang, Tim Tschampel, Ioannis Koutroulis, and Tom Velez. 2019. Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 66–70, Florence, Italy. Association for Computational Linguistics.