TY - GEN
T1 - Development and Evaluation of Machine Learning Models for Recovery Prediction after Treatment for Traumatic Brain Injury
AU - Radabaugh, Hannah L.
AU - Bonnell, Jerry
AU - DIetrich, W. Dalton
AU - Bramlett, Helen M.
AU - Schwartz, Odelia
AU - Sarkar, DIlip
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - Traumatic brain injury (TBI) is a leading cause of death and disability yet treatment strategies remain elusive. Advances in machine learning present exciting opportunities for developing personalized medicine and informing laboratory research. However, their feasibility has yet to be widely assessed in animal research where data are typically limited or in the TBI field where each patient presents with a unique injury. The Operation Brain Trauma Therapy (OBTT) has amassed an animal dataset that spans multiple types of injury, treatment strategies, behavioral assessments, histological measures, and biomarker screenings. This paper aims to analyze these data using supervised learning techniques for the first time by partitioning the dataset into acute input metrics (i.e. 7 days post-injury) and a defined recovery outcome (i.e. memory retention). Preprocessing is then applied to transform the raw OBTT dataset, e.g. developing a class attribute by histogram binning, eliminating borderline cases, and applying principal component analysis (PCA). We find that these steps are also useful in establishing a treatment ranking; Minocycline, a therapy with no significant findings in the OBTT analyses, yields the highest percentage recovery in our ranking. Furthermore, of the seven classifiers we have evaluated, Naïve Bayes achieves the best performance (67%) and yields significant improvement over our baseline model on the preprocessed dataset with borderline elimination. We also investigate the effect of testing on individual treatment groups to evaluate which groups are difficult to classify, and note the interpretive qualities of our model that can be clinically relevant.Clinical Relevance - These studies establish methods for better analyzing multivariate functional recovery and understanding which measures affect prognosis following traumatic brain injury.
AB - Traumatic brain injury (TBI) is a leading cause of death and disability yet treatment strategies remain elusive. Advances in machine learning present exciting opportunities for developing personalized medicine and informing laboratory research. However, their feasibility has yet to be widely assessed in animal research where data are typically limited or in the TBI field where each patient presents with a unique injury. The Operation Brain Trauma Therapy (OBTT) has amassed an animal dataset that spans multiple types of injury, treatment strategies, behavioral assessments, histological measures, and biomarker screenings. This paper aims to analyze these data using supervised learning techniques for the first time by partitioning the dataset into acute input metrics (i.e. 7 days post-injury) and a defined recovery outcome (i.e. memory retention). Preprocessing is then applied to transform the raw OBTT dataset, e.g. developing a class attribute by histogram binning, eliminating borderline cases, and applying principal component analysis (PCA). We find that these steps are also useful in establishing a treatment ranking; Minocycline, a therapy with no significant findings in the OBTT analyses, yields the highest percentage recovery in our ranking. Furthermore, of the seven classifiers we have evaluated, Naïve Bayes achieves the best performance (67%) and yields significant improvement over our baseline model on the preprocessed dataset with borderline elimination. We also investigate the effect of testing on individual treatment groups to evaluate which groups are difficult to classify, and note the interpretive qualities of our model that can be clinically relevant.Clinical Relevance - These studies establish methods for better analyzing multivariate functional recovery and understanding which measures affect prognosis following traumatic brain injury.
UR - http://www.scopus.com/inward/record.url?scp=85091023478&partnerID=8YFLogxK
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U2 - 10.1109/EMBC44109.2020.9175658
DO - 10.1109/EMBC44109.2020.9175658
M3 - Conference contribution
AN - SCOPUS:85091023478
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2416
EP - 2420
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -