Predicting chemotherapy-induced thrombotoxicity by NARX neural networks and transfer learning.

ABSTRACT:

Background

Thrombocytopenia is a common side effect of cytotoxic chemotherapies, which is often dose-limiting. Predicting an individual's risk is of high clinical importance, as otherwise, a small subgroup of patients limits dosages for the overall population for safety reasons.

Methods

We aim to predict individual platelet dynamics using non-linear auto-regressive networks with exogenous inputs (NARX). We consider different architectures of the NARX networks, namely feed-forward networks (FNN) and gated recurrent units (GRU). To cope with the relative sparsity of individual patient data, we employ transfer learning (TL) approaches based on a semi-mechanistic model of hematotoxicity. We use a large data set of patients with high-grade non-Hodgkin's lymphoma to learn the resp

SUBMITTER: Steinacker M 

PROVIDER: S-EPMC11471701 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature