Login / Signup

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.

Carlos Pineda-AntunezClaudia SeguinLuuk A van DuurenAmy B KnudsenBarak DavidiPedro Nascimento de LimaCarolyn RutterKaren M KuntzIris Lansdorp-VogelaarNicholson CollierJonathan OzikFernando Alarid-Escudero
Published in: Medical decision making : an international journal of the Society for Medical Decision Making (2024)
We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process.ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis.This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework.
Keyphrases
  • neural network
  • healthcare
  • public health
  • mental health
  • risk factors
  • low cost
  • climate change
  • risk assessment
  • solid state