Knowing what predicts discontinuation or success of psychotherapies for Borderline Personality Disorder (BPD) is important to improve treatments. Many variables have been reported in the literature, but replication is needed and investigating what therapy process underlies the findings is necessary to understand why variables predict outcome. Using data of an RCT comparing Schema Therapy and Transference Focused Psychotherapy as treatments for BPD, variables derived from the literature were tested as predictors of discontinuation and treatment success. Participants were 86 adult outpatients (80 women, mean age 30.5 years) with a primary diagnosis of BPD who had on average received 3 previous treatment modalities. First, single predictors were tested with logistic regression, controlling for treatment type (and medication use in case of treatment success). Next, with multivariate backward logistic regression essential predictors were detected. Baseline hostility and childhood physical abuse predicted treatment discontinuation. Baseline subjective burden of dissociation predicted a smaller chance of recovery. A second study demonstrated that in-session dissociation, assessed from session audiotapes, mediated the observed effects of baseline dissociation on recovery, indicating that dissociation during sessions interferes with treatment effectiveness. The results suggest that specifically addressing high hostility, childhood abuse, and in-session dissociation might reduce dropout and lack of effectiveness of treatment.
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Several models have been developed to predict prolonged stay in the intensive care unit (ICU) after cardiac surgery. However, no extensive quantitative validation of these models has yet been conducted. This study sought to identify and validate existing prediction models for prolonged ICU length of stay after cardiac surgery.
Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.
