Emulator-based Bayesian calibration of the CISNET colorectal cancer models.

ABSTRACT:

Purpose

To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET) 's SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.

Methods

We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANN) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error

SUBMITTER: Pineda-Antunez C 

PROVIDER: S-EPMC10002763 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature