Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction

Wanyan, Tingyi; Lin, Mingquan; Klang, Eyal; Menon, Kartikeya M.; Gulamali, Faris F; Azad, Ariful; Zhang, Yiye; Ding, Ying; Wang, Zhangyang; Wang, Fei; Glicksberg, Benjamin S.; Peng, Yifan

doi:10.1145/3535508.3545541

Cited by 8 publications

(1 citation statement)

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“…Wanyan et al [173] developed a deep learning model based on contrastive loss to predict critical events such as severe illness, mortality, and intubation that demonstrates promising performance on imbalanced EHR data. They further proposed a unique EHR data heterogeneous feature design training algorithm and combined it with contrastive positive sampling to predict COVID-19 patient mortality [174].…”

Section: Analysis Of Ehrs and Emrsmentioning

confidence: 99%

Advances in the Development of Representation Learning and Its Innovations against COVID-19

Li,

Parvej,

Zhang

et al. 2023

COVID

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In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-dimensional and sparse data, detects complex biological patterns, and optimizes predictive performance. In recent years, graph representation learning has become a popular research topic. It involves the embedding of graphs into a low-dimensional space while preserving the structural and attribute information of the graph, enabling better feature extraction for downstream tasks. This study extensively reviews representation learning advancements, particularly in the research of representation methods since the emergence of COVID-19. We begin with an analysis and classification of neural-network-based language model representation learning techniques as well as graph representation learning methods. Subsequently, we explore their methodological innovations in the context of COVID-19, with a focus on the domains of drugs, public health, and healthcare. Furthermore, we discuss the challenges and opportunities associated with graph representation learning. This comprehensive review presents invaluable insights for researchers as it documents the development of COVID-19 and offers experiential lessons to preempt future infectious diseases. Moreover, this study provides guidance regarding future bioinformatics and biomedicine research methodologies.

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Section: Analysis Of Ehrs and Emrsmentioning

confidence: 99%