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.

A Qualitative Study of Potential Suicide Risk Factors Among Soldiers Transitioning From Operation Iraqi Freedom/Operation Enduring Freedom

Using qualitative methods, this study explored potential risk factors for suicide, as defined by Joiner's Interpersonal-Psychological Theory of Suicide (IPTS), in a population of Soldiers returning from deployment in Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF). Sixty-eight Soldiers participated in semi-structured interviews during the period of transition from deployment to the garrison environment. These Soldiers were asked about changes in perception of pain, experiences of perceived burdensomeness, and lack of belonging. Interviews were transcribed and analyzed. A phenomenological methodology was employed (Creswell, 2006). In response to questions about perception of pain, Soldiers discussed both positive and negative changes in their experience of physical and emotional pain. When asked about experiences of perceived burdensomeness, Soldiers described changes related to deployment, such as injuries and combat related guilt, as well as changes related to transition from combat, including care seeking, reintegration into family and society, and emotional distancing. Regarding the experience of lack of belonging, Soldiers described difficulties related to the deployment, such as combat injuries, leadership roles, and individual differences, as well as difficulties related to reintegration such as symptoms of emotional numbing and distancing. Findings highlight the potential utility of IPTS in exploring both acute and chronic suicide risk factors associated with deployment and transition, as well as potential treatment strategies that may reduce suicide risk in the population of Soldiers during reintegration.

FHWA highway traffic noise prediction model. Final report.

Mode of access: Internet.

User's manual: FHWA highway traffic noise prediction model - SNAP 1.0. Final report.

Federal Highway Administration, Environmental Design and Control Division, Washington, D.C.

User's manual: FHWA Level 2 highway traffic noise prediction model - STAMINA 1.0. Final report.

Federal Highway Administration, Environmental Design and Control Division, Washington, D.C.

User's manual, FHWA level 2 highway traffic noise prediction model, stamina 1.0 /

Mode of access: Internet.

Perceived academic performance, self-esteem and locus of control as indicators of need for assessment of adolescent suicide risk: implications for teachers

Introduction: There is currently a need for research into indicators that could be used by non-clinical professionals working with young people, to inform the need for referral for further clinical assessment of those at risk of suicide. Method: Participants of this repeated measures longitudinal study, were 2603, 2485, and 2246 school students aged 13, 14, and 15, respectively, from 27 South Australian Schools. Results: Perceived academic performance, self-esteem and locus of control are significantly associated with suicidality. Further, logistic regression of longitudinal results suggests that perceived academic performance, over and above self-esteem and locus of control, in some instances, is a good long-term predictor of suicidality. (C) 2004 Published by Elsevier Ltd. on behalf of The Association for Professionals in Services for Adolescents.

Development and evaluation of the Clinician Suicide Risk Assessment Checklist

This paper describes the development and evaluation of a new instrument – the Clinician Suicide Risk Assessment Checklist (CSRAC). The instrument assesses the clinician’s competency in three areas: clinical interviewing, assessment of specific suicide risk factors, and formulating a management plan. A draft checklist was constructed by integrating information from 1) literature review 2) expert clinician focus group and 3) consultation with experts. It was utilised in a simulated clinical scenario with clinician trainees and a trained actor in order to test for inter-rater agreement. Agreement was calculated and the checklist was re-drafted with the aim of maximising agreement. A second phase of simulated clinical scenarios was then conducted and inter-rater agreement was calculated for the revised checklist. In the first phase of the study, 18 of 35 items had inadequate inter-rater agreement (60%>), while in the second phase, using the revised version, only 3 of 39 items failed to achieve adequate inter-rater agreement. Further evidence of reliability and validity are required. Continued development of the CSRAC will be necessary before it can be utilised to assess the effectiveness of risk assessment training programs.

Lifetime suicide risk in major depression: sex and age determinants

Background: Recent work has demonstrated that the lifetime suicide risk for patients with DSM IV Major Depression cannot mathematically approximate the accepted figure of 15%. Gender and age significantly affect both the prevalence of major depression and suicide risk, Methods: Gender and age stratified calculations were made on the entire population of the USA in 1994 using a mathematical algorithm. Sex specific corrections for under-reporting were incorporated into the design. Results: The lifetime suicide risks for men and women were 7% and 1%, respectively. The combined risk was 3.4%. The male:female ratio for suicide risk in major depression was 10:1 for youths under 25, and 5.6:1 for adults. Conclusions: Suicide in major depression is predominantly a male problem, although complacency towards female sufferers is to be avoided. Diagnosis of major depression is of limited help in predicting suicide risk compared to case specific factors. The male experience of depression that leads to suicide is often not identified as a legitimate medical complaint by either sufferers or professionals. Increasing help-accessing by males is a priority. Clinical implications: Patients with a history of hospitalisation; comorbidity, especially for substance abuse; and who are male, require greater vigilance for suicide risk. It may be that for males che threshold for diagnosing and treating major depression needs to be lowered. Limitations: This research is based on a mathematical algorithm to approximate a life-long longitudinal study that identifies community cases of depression. Our findings therefore rely on the validity of the statistics used. Extrapolation is limited to populations with an actual suicide rate of 17/100,000 or less and a lifetime prevalence of major depression of 17% or more. (C) 1999 Elsevier Science B.V. All rights reserved.

A risk-based model for inspection and maintenance of cross-country petroleum pipeline

The existing method of pipeline health monitoring, which requires an entire pipeline to be inspected periodically, is both time-wasting and expensive. A risk-based model that reduces the amount of time spent on inspection has been presented. This model not only reduces the cost of maintaining petroleum pipelines, but also suggests efficient design and operation philosophy, construction methodology and logical insurance plans. The risk-based model uses Analytic Hierarchy Process (AHP), a multiple attribute decision-making technique, to identify the factors that influence failure on specific segments and analyzes their effects by determining probability of risk factors. The severity of failure is determined through consequence analysis. From this, the effect of a failure caused by each risk factor can be established in terms of cost, and the cumulative effect of failure is determined through probability analysis. The technique does not totally eliminate subjectivity, but it is an improvement over the existing inspection method.

Risk-based model aids selection of pipeline inspection, maintenance strategies

The existing method of pipeline monitoring, which requires an entire pipeline to be inspected periodically, wastes time and is expensive. A risk-based model that reduces the amount of time spent on inspection has been developed. This model not only reduces the cost of maintaining petroleum pipelines, but also suggests an efficient design and operation philosophy, construction method and logical insurance plans.The risk-based model uses analytic hierarchy process, a multiple attribute decision-making technique, to identify factors that influence failure on specific segments and analyze their effects by determining the probabilities of risk factors. The severity of failure is determined through consequence analysis, which establishes the effect of a failure in terms of cost caused by each risk factor and determines the cumulative effect of failure through probability analysis.

Risk Prediction In Cardiovascular Epidemiology: Considerations for Modeling Composite Endpoints

Thesis (Master's)--University of Washington, 2016-08