Continual learning of longitudinal health records

Armstrong, J.; Clifton, D.

doi:10.48550/arxiv.2112.11944

Cited by 3 publications

(2 citation statements)

References 21 publications

(31 reference statements)

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“…However, instead of incremental learning, these studies considered domain adaptation for handling distribution shifts. In [31], the authors benchmarked state-ofthe-art domain-incremental learning algorithms for longitudinal electronic health records or multivariate clinical timeseries data and found that replay-based domain-incremental algorithms outperform the other counterparts. On the similar lines, Kiyasseh et al [32] had explored replay-based domainincremental learning on physiological signals earlier.…”

Section: A Incremental Learningmentioning

confidence: 99%

Self-Aware SGD: Reliable Incremental Adaptation Framework for Clinical AI Models

Thakur

Armstrong

Youssef

et al. 2023

IEEE J. Biomed. Health Inform.

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Healthcare is dynamic as demographics, diseases, and therapeutics constantly evolve. This dynamic nature induces inevitable distribution shifts in populations targeted by clinical AI models, often rendering them ineffective. Incremental learning provides an effective method of adapting deployed clinical models to accommodate these contemporary distribution shifts. However, since incremental learning involves modifying a deployed or in-use model, it can be considered unreliable as any adverse modification due to maliciously compromised or incorrectly labelled data can make the model unsuitable for the targeted application. This paper introduces self-aware stochastic gradient descent (SGD), an incremental deep learning algorithm that utilises a contextual bandit-like sanity check to only allow reliable modifications to a model. The contextual bandit analyses incremental gradient updates to isolate and filter unreliable gradients. This behaviour allows self-aware SGD to balance incremental training and integrity of a deployed model. Experimental evaluations on the Oxford University Hospital datasets highlight that self-aware SGD can provide reliable incremental updates for overcoming distribution shifts in challenging conditions induced by label noise.

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Section: A Incremental Learningmentioning

confidence: 99%

Self-Aware SGD: Reliable Incremental Adaptation Framework for Clinical AI Models

Thakur

Armstrong

Youssef

et al. 2023

IEEE J. Biomed. Health Inform.

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“…Separate GAN models were trained on each of the silos, and then were later combined in a central GAN model [62]. We expect future works to investigate the feasibility and introduce new ways to implement GAN models on datasets from different institutions, and explore new applications such as federated and continual learning [178] .…”

Section: Generation Of Ehrs From Multimodal Data and Multi-centersmentioning

confidence: 99%

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

Ghosheh¹,

Li²,

Zhu³

2022

Preprint

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Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to review the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning.

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Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models

Thakur¹,

Armstrong²,

Youssef³

et al. 2023

Preprint

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<p>Healthcare is dynamic as demographics, diseases, and therapeutics constantly evolve. This dynamic nature induces inevitable distribution shifts in populations targeted by clinical AI models, often rendering them ineffective. Incremental learning provides an effective method of adapting deployed clinical models to accommodate these contemporary distribution shifts. However, since incremental learning involves modifying a deployed or in-use model, it can be considered unreliable as any adverse modification due to maliciously compromised or incorrectly labelled data can make the model unsuitable for the targeted application. This paper introduces self-aware stochastic gradient descent (SGD), an incremental deep learning algorithm that utilises a contextual bandit-like sanity check to only allow reliable modifications to a model. The contextual bandit analyses incremental gradient updates to isolate and filter unreliable gradients. This behaviour allows self-aware SGD to balance incremental training and integrity of a deployed model. Experimental evaluations on the Oxford University Hospital datasets highlight that self-aware SGD can provide reliable incremental updates for overcoming distribution shifts in challenging conditions induced by label noise</p>

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Continual learning of longitudinal health records

Cited by 3 publications

References 21 publications

Self-Aware SGD: Reliable Incremental Adaptation Framework for Clinical AI Models

Self-Aware SGD: Reliable Incremental Adaptation Framework for Clinical AI Models

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models

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