Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support

Emilia Apostolova, Tony Wang, Tim Tschampel, Ioannis Koutroulis, Tom Velez

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.
Anthology ID:
W19-5007
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
66–70
Language:
URL:
https://aclanthology.org/W19-5007
DOI:
10.18653/v1/W19-5007
Bibkey:
Cite (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.
Cite (Informal):
Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support (Apostolova et al., BioNLP 2019)
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PDF:
https://preview.aclanthology.org/naacl24-info/W19-5007.pdf
Code
 ema-/patient-context-vectors
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