Back to Search Start Over

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

Authors :
Javidi, Hamed
Mariam, Arshiya
Khademi, Gholamreza
Zabor, Emily C.
Zhao, Ran
Radivoyevitch, Tomas
Rotroff, Daniel M.
Source :
NPJ Digital Medicine; 7/30/2022, Vol. 5 Issue 1, p1-11, 11p
Publication Year :
2022

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23986352
Volume :
5
Issue :
1
Database :
Complementary Index
Journal :
NPJ Digital Medicine
Publication Type :
Academic Journal
Accession number :
158278026
Full Text :
https://doi.org/10.1038/s41746-022-00651-4