TY - JOUR
T1 - Use of machine learning to re-assess patterns of multivariate functional recovery after fluid percussion injury
T2 - Operation brain trauma therapy
AU - Radabaugh, Hannah
AU - Bonnell, Jerry
AU - Schwartz, Odelia
AU - Sarkar, Dilip
AU - Dalton Dietrich, W.
AU - Bramlett, Helen M.
N1 - Funding Information:
These studies were funded by the National Institutes of Health (R01 NS042133), two Department of Defense funding mechanisms (DAMD W81HWH-14-2-0118 and W81XWH-10-1-0623), the University of Miami U-LINK program for interdisciplinary research, and the University of Miami Maytag Graduate Fellowship.
Publisher Copyright:
© Mary Ann Liebert, Inc.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limited methods to capture these individual variabilities. Advances in machine learning (ML) have the potential to further personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in pre-clinical neurotrauma research, where data are strictly limited in subject number. To better establish ML’s feasibility, we utilized the fluid percussion injury (FPI) portion of the rich, rat data set collected by Operation Brain Trauma Therapy (OBTT), which tested multiple pharmacological treatments. Previous work has provided confidence that both unsupervised and supervised ML techniques can uncover useful insights from this OBTT pre-clinical research data set. As a proof-of-concept, we aimed to better evaluate the multi-variate recovery profiles afforded by the administration of nine different experimental therapies. We assessed supervised pairwise classifiers trained on a pre-processed data set that incorporated metrics from four feature groups to determine their ability to correctly identify specific drug treatments. In all but one of the possible pairwise combinations of minocycline, levetiracetam, erythropoietin, nicotinamide, and amantadine, the baseline was outperformed by one or more supervised classifiers, the exception being nicotinamide versus amantadine. Further, when the same methods were employed to assess different doses of the same treatment, the ML classifiers had greater difficulty in understanding which treatment each sample received. Our data serve as a critical first step toward identifying optimal treatments for specific subgroups of samples that are dependent on factors such as types and severity of traumatic injuries, as well as informing the prediction of therapeutic combinations that may lead to greater treatment effects than individual therapies.
AB - Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limited methods to capture these individual variabilities. Advances in machine learning (ML) have the potential to further personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in pre-clinical neurotrauma research, where data are strictly limited in subject number. To better establish ML’s feasibility, we utilized the fluid percussion injury (FPI) portion of the rich, rat data set collected by Operation Brain Trauma Therapy (OBTT), which tested multiple pharmacological treatments. Previous work has provided confidence that both unsupervised and supervised ML techniques can uncover useful insights from this OBTT pre-clinical research data set. As a proof-of-concept, we aimed to better evaluate the multi-variate recovery profiles afforded by the administration of nine different experimental therapies. We assessed supervised pairwise classifiers trained on a pre-processed data set that incorporated metrics from four feature groups to determine their ability to correctly identify specific drug treatments. In all but one of the possible pairwise combinations of minocycline, levetiracetam, erythropoietin, nicotinamide, and amantadine, the baseline was outperformed by one or more supervised classifiers, the exception being nicotinamide versus amantadine. Further, when the same methods were employed to assess different doses of the same treatment, the ML classifiers had greater difficulty in understanding which treatment each sample received. Our data serve as a critical first step toward identifying optimal treatments for specific subgroups of samples that are dependent on factors such as types and severity of traumatic injuries, as well as informing the prediction of therapeutic combinations that may lead to greater treatment effects than individual therapies.
KW - Data analysis
KW - Machine learning
KW - Pharmacotherapy
KW - Traumatic brain injury
UR - http://www.scopus.com/inward/record.url?scp=85107029878&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107029878&partnerID=8YFLogxK
U2 - 10.1089/neu.2020.7357
DO - 10.1089/neu.2020.7357
M3 - Article
C2 - 33107380
AN - SCOPUS:85107029878
VL - 38
SP - 1662
EP - 1669
JO - Central Nervous System Trauma
JF - Central Nervous System Trauma
SN - 0897-7151
IS - 12
ER -