A deep learning approach for length of stay prediction in clinical settings from medical records

Zebin, Tahmina, Rezvy, Shahadate ORCID: https://orcid.org/0000-0002-2684-7117 and Chaussalet, Thierry J. (2019) A deep learning approach for length of stay prediction in clinical settings from medical records. In: 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 09-10 Jul 2019, Siena, Italy.

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Deep neural networks are becoming an increasingly popular solution for predictive modeling using electronic health records because of their capability of learning complex patterns and behaviors from large volumes of patient records. In this paper, we have applied an autoencoded deep neural network algorithm aimed at identifying short(0-7 days) and long stays (>7 days) in hospital based on patient admission records, demographics, diagnosis codes and chart events. We validated our approach using the de-identified MIMIC-III dataset. This proposed autoencoder+DNN model shows that the two classes are separable with 73.2% accuracy based upon ICD-9 and demographics features. Once vital chart events data such as body temperature, blood pressure, heart rate information available after 24 hour of admission is added to the model, the classification accuracy is increased up to 77.7%. Our results showed a better performance when compared to a baseline random forest model.

Item Type: Conference or Workshop Item (Paper)
Keywords (uncontrolled): Deep learning, Electronic Health Records, Clinical Prediction, Length of Stay
Research Areas: A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 27347
Notes on copyright: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Shahadate Rezvy
Date Deposited: 12 Aug 2019 11:26
Last Modified: 17 Sep 2019 16:00
URI: https://eprints.mdx.ac.uk/id/eprint/27347

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