Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation

Survivors of traumatic brain injury (TBI) have an unpredictable clinical course. This unpredictability makes clinical resource allocation for clinicians and anticipatory guidance for patients difficult. Historically, experienced clinicians and traditional statistical models have insufficiently consi...

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Autores principales: Say, Irene, Chen, Yiling Elaine, Sun, Matthew Z., Li, Jingyi Jessica, Lu, Daniel C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Rehabilitation Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535093/
https://www.ncbi.nlm.nih.gov/pubmed/36211830
http://dx.doi.org/10.3389/fresc.2022.1005168
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author Say, Irene
Chen, Yiling Elaine
Sun, Matthew Z.
Li, Jingyi Jessica
Lu, Daniel C.
author_facet Say, Irene
Chen, Yiling Elaine
Sun, Matthew Z.
Li, Jingyi Jessica
Lu, Daniel C.
author_sort Say, Irene
collection PubMed
description Survivors of traumatic brain injury (TBI) have an unpredictable clinical course. This unpredictability makes clinical resource allocation for clinicians and anticipatory guidance for patients difficult. Historically, experienced clinicians and traditional statistical models have insufficiently considered all available clinical information to predict functional outcomes for a TBI patient. Here, we harness artificial intelligence and apply machine learning and statistical models to predict the Functional Independence Measure (FIM) scores after rehabilitation for traumatic brain injury (TBI) patients. Tree-based algorithmic analysis of 629 TBI patients admitted to a large acute rehabilitation facility showed statistically significant improvement in motor and cognitive FIM scores at discharge.
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spelling pubmed-95350932022-10-07 Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation Say, Irene Chen, Yiling Elaine Sun, Matthew Z. Li, Jingyi Jessica Lu, Daniel C. Front Rehabil Sci Rehabilitation Sciences Survivors of traumatic brain injury (TBI) have an unpredictable clinical course. This unpredictability makes clinical resource allocation for clinicians and anticipatory guidance for patients difficult. Historically, experienced clinicians and traditional statistical models have insufficiently considered all available clinical information to predict functional outcomes for a TBI patient. Here, we harness artificial intelligence and apply machine learning and statistical models to predict the Functional Independence Measure (FIM) scores after rehabilitation for traumatic brain injury (TBI) patients. Tree-based algorithmic analysis of 629 TBI patients admitted to a large acute rehabilitation facility showed statistically significant improvement in motor and cognitive FIM scores at discharge. Frontiers Media S.A. 2022-09-22 /pmc/articles/PMC9535093/ /pubmed/36211830 http://dx.doi.org/10.3389/fresc.2022.1005168 Text en © 2022 Say, Chen, Sun, Li and Lu. 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) (https://creativecommons.org/licenses/by/4.0/) . 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 Rehabilitation Sciences
Say, Irene
Chen, Yiling Elaine
Sun, Matthew Z.
Li, Jingyi Jessica
Lu, Daniel C.
Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
title Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
title_full Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
title_fullStr Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
title_full_unstemmed Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
title_short Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
title_sort machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation
topic Rehabilitation Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9535093/
https://www.ncbi.nlm.nih.gov/pubmed/36211830
http://dx.doi.org/10.3389/fresc.2022.1005168
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