The Glasgow Coma Score (GCS) is statistically dubious because its calculation assumes that (a) the diagnostic scores used to assess degree of consciousness are numerical and (b) there is an implied metric. The assessed diagnostic scores are, however, categorical and there exists no metric; hence, summing is neither permitted nor medically informative. Novel methods: In this paper, we statistically analyze the Glasgow Coma Triples (GCTs) of 162 patients (114 males; 48 females; aged 3–93 years) by using unsupervised machine-learning techniques: first, one-hot encoding; second, a dimension reduction autoencoder; and finally KDE (Kernel Density Estimation). Results: We find that this sequence can classify how the resulting segmentation (triage) results in (a) the dead patients clustering separately from the survivors, and (b) the survivors clustering into five groups with different hospital discharge outcomes: from those with GCT={1,1,1} to those with GCT={4,6,5}, albeit with varying trajectories. Conclusions: The use of machine learning techniques can uncover the medical progressions of TBI patients that are impossible to discover using conventional GCS analysis. We also find a triage for outcomes, including five clusters for surviving patients. Further research is needed to verify what medically determines these varying trajectories and their ranges in probabilities; using GCS cannot contribute to these extended investigations, however.
| Published in | Clinical Medicine Research (Volume 11, Issue 6) |
| DOI | 10.11648/j.cmr.20221106.11 |
| Page(s) | 150-158 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Traumatic Brain Injury, Glasgow Coma Score, Kernel Density Estimation, Dimension Reduction, Feature Extraction, Triage, Unsupervised Machine Learning, Glasgow Coma Triples
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APA Style
Hermann Prossinger, Hubert Hetz, Alexandra Acimovic, Reinhard Berger, Karim Mostafa, et al. (2022). A Neural Network Classifies Traumatic Brain Injury Outcomes: Glasgow Coma Triples Are Needed. Clinical Medicine Research, 11(6), 150-158. https://doi.org/10.11648/j.cmr.20221106.11
ACS Style
Hermann Prossinger; Hubert Hetz; Alexandra Acimovic; Reinhard Berger; Karim Mostafa, et al. A Neural Network Classifies Traumatic Brain Injury Outcomes: Glasgow Coma Triples Are Needed. Clin. Med. Res. 2022, 11(6), 150-158. doi: 10.11648/j.cmr.20221106.11
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Download@article{10.11648/j.cmr.20221106.11,
author = {Hermann Prossinger and Hubert Hetz and Alexandra Acimovic and Reinhard Berger and Karim Mostafa and Alexander Grieb and Heinz Steltzer},
title = {A Neural Network Classifies Traumatic Brain Injury Outcomes: Glasgow Coma Triples Are Needed},
journal = {Clinical Medicine Research},
volume = {11},
number = {6},
pages = {150-158},
doi = {10.11648/j.cmr.20221106.11},
url = {https://doi.org/10.11648/j.cmr.20221106.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cmr.20221106.11},
abstract = {The Glasgow Coma Score (GCS) is statistically dubious because its calculation assumes that (a) the diagnostic scores used to assess degree of consciousness are numerical and (b) there is an implied metric. The assessed diagnostic scores are, however, categorical and there exists no metric; hence, summing is neither permitted nor medically informative. Novel methods: In this paper, we statistically analyze the Glasgow Coma Triples (GCTs) of 162 patients (114 males; 48 females; aged 3–93 years) by using unsupervised machine-learning techniques: first, one-hot encoding; second, a dimension reduction autoencoder; and finally KDE (Kernel Density Estimation). Results: We find that this sequence can classify how the resulting segmentation (triage) results in (a) the dead patients clustering separately from the survivors, and (b) the survivors clustering into five groups with different hospital discharge outcomes: from those with GCT={1,1,1} to those with GCT={4,6,5}, albeit with varying trajectories. Conclusions: The use of machine learning techniques can uncover the medical progressions of TBI patients that are impossible to discover using conventional GCS analysis. We also find a triage for outcomes, including five clusters for surviving patients. Further research is needed to verify what medically determines these varying trajectories and their ranges in probabilities; using GCS cannot contribute to these extended investigations, however.},
year = {2022}
}
TY - JOUR
T1 - A Neural Network Classifies Traumatic Brain Injury Outcomes: Glasgow Coma Triples Are Needed
AU - Hermann Prossinger
AU - Hubert Hetz
AU - Alexandra Acimovic
AU - Reinhard Berger
AU - Karim Mostafa
AU - Alexander Grieb
AU - Heinz Steltzer
Y1 - 2022/11/11
PY - 2022
N1 - https://doi.org/10.11648/j.cmr.20221106.11
DO - 10.11648/j.cmr.20221106.11
T2 - Clinical Medicine Research
JF - Clinical Medicine Research
JO - Clinical Medicine Research
SP - 150
EP - 158
PB - Science Publishing Group
SN - 2326-9057
UR - https://doi.org/10.11648/j.cmr.20221106.11
AB - The Glasgow Coma Score (GCS) is statistically dubious because its calculation assumes that (a) the diagnostic scores used to assess degree of consciousness are numerical and (b) there is an implied metric. The assessed diagnostic scores are, however, categorical and there exists no metric; hence, summing is neither permitted nor medically informative. Novel methods: In this paper, we statistically analyze the Glasgow Coma Triples (GCTs) of 162 patients (114 males; 48 females; aged 3–93 years) by using unsupervised machine-learning techniques: first, one-hot encoding; second, a dimension reduction autoencoder; and finally KDE (Kernel Density Estimation). Results: We find that this sequence can classify how the resulting segmentation (triage) results in (a) the dead patients clustering separately from the survivors, and (b) the survivors clustering into five groups with different hospital discharge outcomes: from those with GCT={1,1,1} to those with GCT={4,6,5}, albeit with varying trajectories. Conclusions: The use of machine learning techniques can uncover the medical progressions of TBI patients that are impossible to discover using conventional GCS analysis. We also find a triage for outcomes, including five clusters for surviving patients. Further research is needed to verify what medically determines these varying trajectories and their ranges in probabilities; using GCS cannot contribute to these extended investigations, however.
VL - 11
IS - 6
ER -
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