Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F24%3A10500427" target="_blank" >RIV/00216208:11110/24:10500427 - isvavai.cz</a>

Výsledek na webu

<a href="https://proceedings.mlr.press/v252/trottet24a.html" target="_blank" >https://proceedings.mlr.press/v252/trottet24a.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis

Popis výsledku v původním jazyce

We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the underlying generative process that explain the observed patient disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical knowledge. By combining the generative approach with medical definitions of different characteristics of SSc, we facilitate the discovery of new aspects of the disease. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering SSc patient trajectories into novel sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series with uncertainty quantification.

Název v anglickém jazyce

Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis

Popis výsledku anglicky

We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the underlying generative process that explain the observed patient disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical knowledge. By combining the generative approach with medical definitions of different characteristics of SSc, we facilitate the discovery of new aspects of the disease. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering SSc patient trajectories into novel sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series with uncertainty quantification.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

30226 - Rheumatology

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 9th Machine Learning for Healthcare Conference

ISBN

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ISSN

2640-3498

e-ISSN

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Počet stran výsledku

42

Strana od-do

1-42

Název nakladatele

ML Research Press

Místo vydání

San Diego

Místo konání akce

Toronto

Datum konání akce

16. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001483857800033