An Overview of Augmenting AI Application in Healthcare

Chellasamy, Aarthy; Nagarathinam, Aishwarya

doi:10.1007/978-981-19-0898-9_31

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…These benefits are highly valuable within various domains. For example, generated synthetic data has been shown to benefit significantly healthcare and medicine fields (Abbasimehr, Paki, and Bahrini 2022;Chellasamy and Nagarathinam 2022;Er, Yang, and Zhao 2023;Kiyasseh et al 2020), by addressing the concerns about data privacy and restricted data access limit, and thus boosting model comparisons and the development of new models.…”

Section: Introductionmentioning

confidence: 99%

Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow

Li,

Wu,

Shi

et al. 2024

AAAI

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Time-series generation has crucial practical significance for decision-making under uncertainty. Existing methods have various limitations like accumulating errors over time, significantly impacting downstream tasks. We develop a novel generation method, DT-VAE, that incorporates generalizable domain knowledge, is mathematically justified, and significantly outperforms existing methods by mitigating error accumulation through a cumulative difference learning mechanism. We evaluate the performance of DT-VAE on several downstream tasks using both semi-synthetic and real time-series datasets, including benchmark datasets and our newly curated COVID-19 hospitalization datasets. The COVID-19 datasets enrich existing resources for time-series analysis. Additionally, we introduce Diverse Trend Preserving (DTP), a time-series clustering-based evaluation for direct and interpretable assessments of generated samples, serving as a valuable tool for evaluating time-series generative models.

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Section: Introductionmentioning

confidence: 99%