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Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data...

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Autores principales: Shickel, Benjamin, Davoudi, Anis, Ozrazgat-Baslanti, Tezcan, Ruppert, Matthew, Bihorac, Azra, Rashidi, Parisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954405/
https://www.ncbi.nlm.nih.gov/pubmed/33718920
http://dx.doi.org/10.3389/fdgth.2021.640685
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author Shickel, Benjamin
Davoudi, Anis
Ozrazgat-Baslanti, Tezcan
Ruppert, Matthew
Bihorac, Azra
Rashidi, Parisa
author_facet Shickel, Benjamin
Davoudi, Anis
Ozrazgat-Baslanti, Tezcan
Ruppert, Matthew
Bihorac, Azra
Rashidi, Parisa
author_sort Shickel, Benjamin
collection PubMed
description Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.
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spelling pubmed-79544052021-03-12 Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU Shickel, Benjamin Davoudi, Anis Ozrazgat-Baslanti, Tezcan Ruppert, Matthew Bihorac, Azra Rashidi, Parisa Front Digit Health Digital Health Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients. Frontiers Media S.A. 2021-02-22 /pmc/articles/PMC7954405/ /pubmed/33718920 http://dx.doi.org/10.3389/fdgth.2021.640685 Text en Copyright © 2021 Shickel, Davoudi, Ozrazgat-Baslanti, Ruppert, Bihorac and Rashidi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Digital Health
Shickel, Benjamin
Davoudi, Anis
Ozrazgat-Baslanti, Tezcan
Ruppert, Matthew
Bihorac, Azra
Rashidi, Parisa
Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU
title Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU
title_full Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU
title_fullStr Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU
title_full_unstemmed Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU
title_short Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU
title_sort deep multi-modal transfer learning for augmented patient acuity assessment in the intelligent icu
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954405/
https://www.ncbi.nlm.nih.gov/pubmed/33718920
http://dx.doi.org/10.3389/fdgth.2021.640685
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