Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

By Xiangao Jiang, M. Coffee, Anasse Bari, Junzhang Wang, Xinyue Jiang, Jianping Huang, Jichan Shi, J. Dai, Jiong Cai, Tianxiao Zhang and 3 more. 2020

379 citations recorded by the source corpus.

The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19 Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases

Subfields: Artificial Intelligence, Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/703eaf607de6872b13225e053a4e15ad90440a8e

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Citations in the corpus

Artificial intelligence vs COVID-19: limitations, constraints and pitfalls (2020, background): “Lazer et al. (2014) referred to these as “big data hubris and algorithm dynamics”.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “In this vein, there have been several recent papers that propose triage approaches based on features contained in patients' medical data and blood tests, in order to help clinicians identify high-risk patients and those at risk of later development of ARDS [87, 88] † , [89] .”
A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact (2020, methodology): “The authors of [191] have also proposed an AI framework that leverages predictive analysis performed on real COVID-19 patients to support clinical decision-making.”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “Then the predicted value for the next step is obtained based on the following equations [34] : where and . . . where m = 1 .”
Machine learning based approaches for detecting COVID-19 using clinical text data (2020, background): “[14] proposed a machine learning model that can predict a person affected with COVID-19 and has the possibility to develop acute respiratory distress syndrome (ARDS).”
Pulmonary Arterial Thrombosis in COVID-19 With Fatal Outcome: Results From a Prospective, Single-Center, Clinicopathologic Case Series (2020, background): “sis of subsegmental and segmental pulmonary arteries (44, 45).”