Constructing training set using distance between learnt graphical models of time series data on patient physiology, to predict disease scores.

ABSTRACT: Interventional endeavours in medicine include prediction of a score that parametrises a new subject's susceptibility to a given disease, at the pre-onset stage. Here, for the first time, we provide reliable learning of such a score in the context of the potentially-terminal disease VOD, that often arises after bone marrow transplants. Indeed, the probability of surviving VOD, is correlated with early intervention. In our work, the VOD-score of each patient in a retrospective cohort, is defined as the distance between the (posterior) probability of a random graph variable-given the inter-variable partial correlation matrix of the time series data on variables that represent different aspects of patient physiology-and that given such time series data of an arbitrarily-selected reference pati

SUBMITTER: Chakrabarty D 

PROVIDER: S-EPMC10586698 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature