A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Third complementarity-determining region of mutated V(H) immunoglobulin genes contains shorter V, D, J, P, and N components than non-mutated genes

The third complementarity-determining region (CDR3) of immunoglobulin variable genes for the heavy chain (V(H)) has been shown to be shorter in length in hypermutated antibodies than in non-hypermutated antibodies. To determine which components of CDR3 contribute to the shorter length, and if there is an effect of age on the length, we analysed 235 cDNA clones from human peripheral blood of V(H)6 genes rearranged to immunoglobulin M (IgM) constant genes. There was similar use of diversity (D) and joining (J(H)) gene segments between clones from young and old donors, and there was similar use of D segments among the mutated and non-mutated heavy chains. However, in the mutated heavy chains, there was increased use of shorter J(H)4 segments and decreased use of longer J(H)6 segments compared to the non-mutated proteins. The overall length of CDR3 did not change with age within the mutated and non-mutated categories, but was significantly shorter by three amino acids in the mutated clones compared to the non-mutated clones. Analyses of the individual components that comprise CDR3 indicated that they were all shorter in the mutated clones. Thus, there were more nucleotides deleted from the ends of V(H), D, and J(H) gene segments, and fewer P and N nucleotides added. The results suggest that B cells bearing immunoglobulin receptors with shorter CDR3s have been selected for binding to antigen. A smaller CDR3 may allow room in the antibody binding pocket for antigen to interact with CDRs 1 and 2 as well, so that as the VDJ gene undergoes hypermutation, substitutions in all three CDRs can further contribute to the binding energy