An Operational Planning Information System For Small Communities : Draft Copy

Maxman, Robert J.; Grecco, W L

doi:10.5703/1288284313767

Cited by 1 publication

(1 citation statement)

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“…The pandemic illustrated that the current metrics used to measure the preparedness of healthcare systems should change as they did not reflect performance during the pandemic. For example, the US was ranked atop the Global Health Security Index, yet it was one of the hardest-hit countries [3]. There is expert consensus that we will have an increasing number of pandemics in the future as our world becomes more connected.…”

Section: Introductionmentioning

confidence: 99%

RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness

Youssef

Zhu

Thakur

et al. 2022

Preprint

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COVID-19 is unlikely to be the last pandemic that we face. According to an analysis of a global dataset of historical pandemics from 1600 to the present, the risk of a COVID-like pandemic has been estimated as 2.63% annually or a 38% lifetime probability. This rate may double over the coming decades. While we may be unable to prevent future pandemics, we can reduce their impact by investing in preparedness. In this study, we propose RapiD_AI: a framework to guide the use of pretrained neural network models as a pandemic preparedness tool to enable healthcare system resilience and effective use of ML during future pandemics. The RapiD_AI framework allows us to build high-performing ML models using data collected in the first weeks of the pandemic and provides an approach to adapt the models to the local populations and healthcare needs. The motivation is to enable healthcare systems to overcome data limitations that prevent the development of effective ML in the context of novel diseases. We digitally recreated the first 20 weeks of the COVID-19 pandemic and experimentally demonstrated the RapiD_AI framework using domain adaptation and inductive transfer. We (i) pretrain two neural network models (Deep Neural Network and TabNet) on a large Electronic Health Records dataset representative of a general in-patient population in Oxford, UK, (ii) fine-tune using data from the first weeks of the pandemic, and (iii) simulate local deployment by testing the performance of the models on a held-out test dataset of COVID-19 patients. Our approach has demonstrated an average relative/absolute gain of 4.92/4.21% AUC compared to an XGBoost benchmark model trained on COVID-19 data only. Moreover, we show our ability to identify the most useful historical pretraining samples through clustering and to expand the task of deployed models through inductive transfer to meet the emerging needs of a healthcare system without access to large historical pretraining datasets.

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Section: Introductionmentioning

confidence: 99%