Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning.

ABSTRACT:

Background

In clinical practice, a plethora of medical examinations are conducted to assess the state of a patient's pathology producing a variety of clinical data. However, investigation of these data faces two major challenges. Firstly, we lack the knowledge of the mechanisms involved in regulating these data variables, and secondly, data collection is sparse in time since it relies on patient's clinical presentation. The former limits the predictive accuracy of clinical outcomes for any mechanistic model. The latter restrains any machine learning algorithm to accurately infer the corresponding disease dynamics.

Methods

Here, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning, aiming at improving individualized predictio

SUBMITTER: Mascheroni P 

PROVIDER: S-EPMC9053281 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature