Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and …
Background There is growing concern of health effects of exposure to pollutant mixtures. We
initially proposed an Environmental Risk Score (ERS) as a summary measure to examine …
initially proposed an Environmental Risk Score (ERS) as a summary measure to examine …
Combustion system optimization of a light-duty GCI engine using CFD and machine learning
In this study, the combustion system of a light-duty compression ignition engine running on a
market gasoline fuel with Research Octane Number (RON) of 91 was optimized using …
market gasoline fuel with Research Octane Number (RON) of 91 was optimized using …
Heterogeneity in cognitive disability after a major disaster: A natural experiment study
Cognitive disability following traumatic experiences of disaster has been documented;
however, little is known about heterogeneity in the association across individuals. In this …
however, little is known about heterogeneity in the association across individuals. In this …
Uncovering heterogeneous associations between disaster-related trauma and subsequent functional limitations: a machine-learning approach
This study examined heterogeneity in the association between disaster-related home loss
and functional limitations of older adults, and identified characteristics of vulnerable …
and functional limitations of older adults, and identified characteristics of vulnerable …
Associations of hypothetical early intensive in-hospital rehabilitation with activities of daily living after hip fracture surgery in patients with and without dementia …
Objectives To investigate the impact of early intensive in-hospital rehabilitation, initiated
within 2 days of surgery and lasting up to 7 days, on the recovery of activities of daily living in …
within 2 days of surgery and lasting up to 7 days, on the recovery of activities of daily living in …
Constrained binary classification using ensemble learning: an application to cost‐efficient targeted PrEP strategies
W Zheng, L Balzer, M van der Laan… - Statistics in …, 2018 - Wiley Online Library
Binary classification problems are ubiquitous in health and social sciences. In many cases,
one wishes to balance two competing optimality considerations for a binary classifier. For …
one wishes to balance two competing optimality considerations for a binary classifier. For …
Evaluating treatment effectiveness under model misspecification: a comparison of targeted maximum likelihood estimation with bias-corrected matching
Statistical approaches for estimating treatment effectiveness commonly model the endpoint,
or the propensity score, using parametric regressions such as generalised linear models …
or the propensity score, using parametric regressions such as generalised linear models …
Simulation-based design of pragmatic trials in psoriatic arthritis using propensity scores
SM Weinstein, LC Coates, PS Helliwell… - Clinical …, 2021 - journals.sagepub.com
Background/Aims: Design of clinical trials requires careful decision-making across several
dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial …
dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial …
[PDF][PDF] Computing a branch's total added value from incomplete annual accounting data using machine learning techniques
T Marchal - libstore.ugent.be
Abstract The National Bank of Belgium publishes (sectoral) studies on employment,
economic value added and other important economic variables on a yearly basis. A main …
economic value added and other important economic variables on a yearly basis. A main …