A deep state-space analysis framework for cancer patient latent state estimation and classification from EHR time-series data.

ABSTRACT: Advancements in deep learning technologies and an increase in medical data have enhanced the accuracy of disease diagnosis and treatment strategies. Notably, significant progress has been made in the use of deep learning-based time-series prediction models for short-term disease onset prediction and analysis of important features. However, research on explainable deep learning for long-term disease progression, such as cancer and chronic diseases, still faces challenges. The difficulty in estimating explainable gradual disease progression from observable patient test data is a key factor. To address this issue, we propose a new approach called the "deep state-space analysis framework." This framework utilizes sequentially obtained electronic health records (EHRs) to estimate and visualize

SUBMITTER: Okamoto Y 

PROVIDER: S-EPMC12858016 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature