Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Long-term foliar persistence and efficacy of spinosad against beet armyworm under greenhouse conditions.

BACKGROUND: The immediate lethality caused by spinosad has been widely studied on Spodoptera exigua (H ¿ ubner). However, long-term effects can also provide valuable information on insecticide toxic action. Here, the persistence of spinosad on Capsicum annuum L. foliage and the lethal and sublethal effects of greenhouse-aged foliar residues of this insecticide on third instars of S. exigua are reported. RESULTS: Foliage was collected at 0, 3, 5, 10, 20, 30, 40 and 50 days after application, and spinosad residues were measured. Residues decreased over time according to first-order kinetics. The average rate constant and half-life of disappearance were 4.44×10?3 and156 daysand5.80×10?3 and120 days for60and120 mg L?1 respectively. Larval mortalitygradually decreased, corresponding to the residues, but was still appreciable (35 and 65% for 60 and 120 mg L?1 respectively) when the larvae were fed with foliage collected 50 days after treatment. Subsequently, pupal development was reduced and varied between 20 and 60% and between 21 and 41% for 60 and 120 mg L?1, respectively, in all ages of leaf residues that were bioassayed. At all time points, the consumption rate by the larvae was reduced between 62 and 84% for both concentrations that were bioassayed. CONCLUSION: It is concluded that, under the present greenhouse conditions, the degradation of spinosad was slower than that reported by other authors in the field, and, because of that, its residues could cause lethal and sublethal effects to S. exigua larvae.