Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study

Sadati, Najibesadat; Nezhad, Milad Zafar; Chinnam, Ratna Babu; Zhu, Dongxiao

doi:10.48550/arxiv.1801.02961

Cited by 1 publication

(1 citation statement)

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“…Autoencoders have also been effectively applied to datasets comprising massive collections of electronic health records, where they are very adept at handling missing data [ 34 ]. A comparison study by Sadati et al [ 35 ] emphasised the effectiveness of several types of autoencoders for electronic health record-based data sets. Combining a recurrent autoencoder with two GANs, Lee et al [ 36 ] suggested sequential electronic health records with a dual adversarial autoencoder (DAAE).…”

Section: Related Workmentioning

confidence: 99%

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

Khodadadi

Bousejin²,

Molaei

et al. 2023

Sensors

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An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations.

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Section: Related Workmentioning

confidence: 99%