Parental Rejection, Personality, and Depression in the Prediction of Suicidality in a Sample of Nonclinical Young Adults

This study tested a prediction model of suicidality in a sample of young adults. Predictor variables included perceived parental rejection, self-criticism, neediness, and depression. Participants (N 5 165) responded to the Depressive Experiences Questionnaire,theInventoryforAssessingMemoriesofParentalRearingBehavior, theCenterforEpidemiologicalStudiesDepressionScale,andtheSuicideBehaviors Questionnaire—Revised. Perceived parental rejection, personality, and depression wereassessedinitiallyatTime1,anddepressionagainandsuicidalitywereassessed 5 months later at Time 2. The proposed structural equation model fit the observed data well in a sample of young adults. Parental rejection demonstrated direct and indirect relationships with suicidality, and self-criticism and neediness each had indirect associations with suicidality. Depression was directly related to suicidality. Implications for clinical practice are discussed.

New measures for assessing model equilibrium and prediction mismatch in species distribution models

Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accuracy is then evaluated through a number of measures that have been the subject of recent reviews. We propose four additional measures that analyse observation-prediction mismatch from a different angle – namely, from the perspective of the predicted rather than the observed area – and add to the existing toolset of model evaluation methods. We explain how these measures can complete the view provided by the existing measures, allowing further insights into distribution model predictions. We also describe how they can be particularly useful when using models to forecast the spread of diseases or of invasive species and to predict modifications in species’ distributions under climate and land-use change

Prediction of Length of Hospital Stay in Preterm Infants - A Case-Based Reasoning View

The length of stay of preterm infants in a neonatology service has become an issue of a growing concern, namely considering, on the one hand, the mothers and infants health conditions and, on the other hand, the scarce healthcare facilities own resources. Thus, a pro-active strategy for problem solving has to be put in place, either to improve the quality-of-service provided or to reduce the inherent financial costs. Therefore, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a case-based problem solving methodology to computing, that caters for the handling of incomplete, unknown, or even contradictory in-formation. The proposed model has been quite accurate in predicting the length of stay (overall accuracy of 84.9%) and by reducing the computational time with values around 21.3%.