Distinguishing compact binary population synthesis models using gravitational-wave observations of coalescing binary black holes

The coalescence of compact binaries containing neutron stars or black holes is one of the most promising signals for advanced ground-based laser interferometer gravitational-wave detectors, with the first direct detections expected over the next few years. The rate of binary coalescences and the distribution of component masses is highly uncertain, and population synthesis models predict a wide range of plausible values. Poorly constrained parameters in population synthesis models correspond to poorly understood astrophysics at various stages in the evolution of massive binary stars, the progenitors of binary neutron star and binary black hole systems. These include effects such as supernova kick velocities, parameters governing the energetics of common envelope evolution and the strength of stellar winds. Observing multiple binary black hole systems through gravitational waves will allow us to infer details of the astrophysical mechanisms that lead to their formation. Here we simulate gravitational-wave observations from a series of population synthesis models including the effects of known selection biases, measurement errors and cosmology. We compare the predictions arising from different models and show that we will be able to distinguish between them with observations (or the lack of them) from the early runs of the advanced LIGO and Virgo detectors. This will allow us to narrow down the large parameter space for binary evolution models.Comment: 16 pages, 8 figures, updated to match version published in Ap

Chronic Disease Risk Prediction Models and their Impacts on Behavioural and Health Outcomes: A Systematic Review and Meta-analysis

Risk prediction models are tools that predict an individual’s risk of developing a health outcome. They were developed to influence patient management by guiding preventive interventions, with the goal of reducing the incidence of new diseases. Studies examining their impacts are uncommon, and no consensus regarding their effects has been reached. This systematic review sought to determine the impact of risk prediction models for chronic diseases on physician behaviour, patient behaviour, and patient health outcomes. Twenty-two studies were found to be eligible for review. The results demonstrated that: 1) physician behaviour may be positively influenced, though a statistically significant result was not found; 2) alterations in patient behaviour were inconclusive; and 3) some aspects of patient health outcomes were significantly improved, such as changes in blood pressure, but these results may be clinically insignificant. The evidence indicates some effects may exist, though future studies are required to confirm this effect

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the very high risk category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt