Dataset Information

Identification of robust deep neural network models of longitudinal clinical measurements.

ABSTRACT: Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness). Overall, discrimination based on variation in shape was more challenging than magnitude. Time-series forest-convolutional neural networks (TSF-CNN) and Gramian angular field(GAF)-CNN outperformed other approaches (P < 0.05) with overall area-under-the-curve (AUCs) of 0.93 for both models, and 0.92 and 0.89 for variation in magnitude and shape with up to 50% missing data. Furthermore, in a real-world assessment, the TSF-CNN model predicted T2D with AUCs reaching 0.72 using only BMI trajectories. In conclusion, we performed an extensive evaluation of DL approaches and identified robust modeling frameworks for disease prediction based on longitudinal clinical measurements.

SUBMITTER: Javidi H

PROVIDER: S-EPMC9329311 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Identification of robust deep neural network models of longitudinal clinical measurements.

Javidi Hamed H Mariam Arshiya A Khademi Gholamreza G Zabor Emily C EC Zhao Ran R Radivoyevitch Tomas T Rotroff Daniel M DM

NPJ digital medicine 20220727 1

Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness). Overall, discrimination based on va ...[more]

PMID: 35896817

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Dataset Information

Identification of robust deep neural network models of longitudinal clinical measurements.

Publications

Identification of robust deep neural network models of longitudinal clinical measurements.

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