Differential benefits of cardiac care regionalization based on driving time to percutaneous coronary intervention

Shen, Yu‐Chu; Hsia, Renee Y.

doi:10.1111/acem.14195

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…The serviced grid cells of each hospital are determined as grid cells which are within 30 minutes driving time to the hospital. The threshold of 30-minute driving time is selected because it is a frequently adopted threshold to determine the serviced patients of the hospital [46][47][48][49] . The estimated shortest distance between grid cells is calculated following the work of et al 50 .…”

Section: Measurement Of Redundancymentioning

confidence: 99%

Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features

Mostafavi,

kai

2023

Preprint

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Community resilience is a complex and muti-faceted phenomenon that emerges from complex and nonlinear interactions among different socio-technical systems and their resilience properties. However, present studies on community resilience focus primarily on vulnerability assessment and utilize index-based approaches, with limited ability to capture heterogeneous features within community socio-technical systems and their nonlinear interactions in shaping robustness, redundancy, and resourcefulness components of resilience. To address this gap, this paper presents an integrated three-layer deep learning model for community resilience rating (called Resili-Net). Twelve measurable resilience features are specified and computed within community socio-technical systems (i.e., facilities, infrastructures, and society) related to three resilience components of robustness, redundancy, and resourcefulness. Using publicly accessible data from multiple metropolitan statistical areas in the United States, Resili-Netcharacterizes the resilience levels of spatial areas into five distinct levels. The interpretability of the model outcomes enables feature analysis for specifying the determinants of resilience in areas within each resilience level, allowing for the identification of specific resilience enhancement strategies. Changes in community resilience profiles under urban development patterns are further examined by changing the value of related socio-technical systems features. Also, combined resilience-risk levels in each community are analyzed, and several communities are found to suffer from high risk and low resilience, which calls for special attention to resilience enhancement. Departing from the dominantly vulnerability-focused assessments, Resili-Net enables characterizing community resilience as an emergent property arising from nonlinear interactions among heterogeneous community features related to their socio-technical systems. Accordingly, the outcomes provide novel perspectives for community resilience assessment by harnessing machine intelligence and heterogeneous urban big data.

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