The rising prevalence of nervous system disorders has become a significant global health challenge, with environmental pollutants identified as key contributors. However, the large number of environmental related compounds, combined with the low effi...
In longitudinal observational studies with time-varying confounders, the generalized computation algorithm formula (g-formula) is a principled tool to estimate the average causal effect of a treatment regimen. However, the standard non-iterative g-fo...
Modeling with multiomics data presents multiple challenges, such as the high dimensionality of the problem ( ), the presence of interactions between features, and the need for integration between multiple data sources. We establish an interaction mo...
OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Record (EHR) data.
BACKGROUND: In a wide range of diseases, it is necessary to utilize multiple data sources to obtain enough data for model training. However, performing centralized pooling of multiple data sources, while protecting each patients' sensitive data, can ...
Over the years, toxicity prediction has been a challenging task. Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. An optimized ensembled model is used to contrast t...
BMC medical informatics and decision making
May 16, 2025
BACKGROUND: As personalized medicine becomes more prevalent, the objective measurement and visualization of an individual's health status are becoming increasingly crucial. However, as the dimensions of data collected from each individual increase, t...
BACKGROUND: Accurate prediction of population-wide depression incidence is vital for effective public mental health management. However, this incidence is often influenced by socioeconomic factors, such as abrupt events or changes, including pandemic...
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...
The journal of applied laboratory medicine
Mar 3, 2025
BACKGROUND: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study d...
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