AIMC Topic: Computer Simulation

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A Bayesian Approach to the G-Formula via Iterative Conditional Regression.

Statistics in medicine
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...

Integrating Multiple Data Sources With Interactions in Multi-Omics Using Cooperative Learning.

Statistics in medicine
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...

Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

Pharmacoepidemiology and drug safety
PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) usi...

A novel in silico approach for predicting unbound brain-to-plasma ratio using machine learning-based support vector regression.

Computers in biology and medicine
The blood-brain barrier (BBB) functions as a vital protective mechanism, restricting the entry of substances and xenobiotics into the central nervous system (CNS). Consequently, BBB penetration is a critical aspect of absorption, distribution, metabo...

A Global Visual Information Intervention Model for Medical Visual Question Answering.

Computers in biology and medicine
Medical Visual Question Answering (Med-VQA) aims to furnish precise responses to clinical queries related to medical imagery. While its transformative potential in healthcare is undeniable, current solutions remain nascent and are yet to see widespre...

A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Electrophysiological source imaging (ESI) is a challenging technique for noninvasively measuring brain activity, which involves solving a highly ill-posed inverse problem. Traditional methods attempt to address this challen...

Deep reinforcement learning for Type 1 Diabetes: Dual PPO controller for personalized insulin management.

Computers in biology and medicine
BACKGROUND: Managing blood glucose levels in Type 1 Diabetes Mellitus (T1DM) is essential to prevent complications. Traditional insulin delivery methods often require significant patient involvement, limiting automation. Reinforcement Learning (RL)-b...

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

Computer methods and programs in biomedicine
OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage remains debated. This study aims to determine the optimal multi-stage fluid resuscitation dosage for sepsis patients.

Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models.

IEEE transactions on neural networks and learning systems
The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processe...

STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication opera...