Article

Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia Risk

2024-01-20

Abstract excerpt

<h4>Objective</h4> Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, this assumption is rarely true for most diseases, as the underlying factors have complex, non-linear, and time-varying relationships. This concern is especial...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a419ad98-5d69-5e53-ae94-35aa6a490e77
DOI: 10.1101/2024.01.18.24301456

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia RiskDOI 10.1101/2024.01.18.24301456Read

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine LearningSemantic proximity Read

Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learningSemantic proximity Read

Hierarchical representation learning of preeclampsia interactome connecting endometrial maturation, placentation, chorioamnionitis, and HELLP syndromeSemantic proximity Read

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk ScoresSemantic proximity Read

Machine Learning for Dynamic and Short-term Prediction of Preeclampsia Using Routine Clinical and Laboratory DataSemantic proximity Read

Rethinking Risk Prediction in Preeclampsia: From Biomarkers to Mechanistic Phenotypes and Longitudinal Models.Semantic proximity Read

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late PregnanciesSemantic proximity Read

Phenotypic subclassification of preeclampsia through cluster analysis of preterm birth-related factors.Semantic proximity Read

Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birthSemantic proximity Read

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infectionSemantic proximity Read

A New Model for Screening for Late-Onset Preeclampsia in the Third TrimesterSemantic proximity Read

Development and validation of a neural network-based survival model for mortality in ischemic heart diseaseSemantic proximity Read