A survey on active learning: State-of-the-art, practical challenges and research directions
Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …
A systematic literature review on AutoML for multi-target learning tasks
Automated machine learning (AutoML) aims to automate machine learning (ML) tasks,
eliminating human intervention from the learning process as much as possible. However …
eliminating human intervention from the learning process as much as possible. However …
The impact of vaccination to control COVID-19 burden in the United States: A simulation modeling approach
Introduction Vaccination programs aim to control the COVID-19 pandemic. However, the
relative impacts of vaccine coverage, effectiveness, and capacity in the context of …
relative impacts of vaccine coverage, effectiveness, and capacity in the context of …
Calibration and validation of the colorectal cancer and adenoma incidence and mortality (CRC-AIM) microsimulation model using deep neural networks
Objectives Machine learning (ML)–based emulators improve the calibration of decision-
analytical models, but their performance in complex microsimulation models is yet to be …
analytical models, but their performance in complex microsimulation models is yet to be …
Bayesian methods for calibrating health policy models: a tutorial
Mathematical simulation models are commonly used to inform health policy decisions.
These health policy models represent the social and biological mechanisms that determine …
These health policy models represent the social and biological mechanisms that determine …
High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
Background Cancer is a complex, multiscale dynamical system, with interactions between
tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host …
tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host …
The University of Wisconsin breast cancer epidemiology simulation model: an update
The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also
referred to as Model W, is a discrete-event microsimulation model that uses a systems …
referred to as Model W, is a discrete-event microsimulation model that uses a systems …
Innovations in integrating machine learning and agent-based modeling of biomedical systems
Agent-based modeling (ABM) is a well-established computational paradigm for simulating
complex systems in terms of the interactions between individual entities that comprise the …
complex systems in terms of the interactions between individual entities that comprise the …
Extreme-scale dynamic exploration of a distributed agent-based model with the EMEWS framework
Agent-based models (ABMs) integrate the multiple scales of behavior and data to produce
higher order dynamic phenomena and are increasingly used in the study of important social …
higher order dynamic phenomena and are increasingly used in the study of important social …
Choosing a metamodel of a simulation model for uncertainty quantification
TM de Carvalho, J van Rosmalen… - Medical decision …, 2022 - journals.sagepub.com
Background Metamodeling may substantially reduce the computational expense of
individual-level state transition simulation models (IL-STM) for calibration, uncertainty …
individual-level state transition simulation models (IL-STM) for calibration, uncertainty …