Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RCTs are not feasible, large observational data sets emerge as a pivotal resource, though these data s...
Studies in health technology and informatics
39176559
Causal inference seeks to learn the effect of interventions on outcomes. Its potential in the health domain has been dramatically increasing recently, due to advancements in machine learning, as well as in the growing amount of medical data collected...
Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that...
In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimate...
Neural networks : the official journal of the International Neural Network Society
39778296
Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text ins...
Neural networks : the official journal of the International Neural Network Society
39742537
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neura...
Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effecti...
Targeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these mo...
BACKGROUND: Complex public health problems have been addressed in communities through systems thinking and participatory methods like Group Model Building (GMB) and Causal Loop Diagrams (CLDs) albeit with some challenges. This study aimed to explore ...
Pflugers Archiv : European journal of physiology
39470762
With rapid advances of deep neural networks over the past decade, artificial intelligence (AI) systems are now commonplace in many applications in biomedicine. These systems often achieve high predictive accuracy in clinical studies, and increasingly...