AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

Santosh, K. C.

doi:10.1007/s10916-020-01562-1

Cited by 322 publications

(232 citation statements)

References 17 publications

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“…Moreover, the calculator is trained and tested on the patient cohort of interest and can account for the evolving nature of this pandemic by daily or more frequent updates and model retraining. 29 The proposed calculator has some limitations. It was designed to be linear with only essential predictors included, and non-linear or convolutional/recurrent models may provide improved performance.…”

Section: Discussionmentioning

confidence: 99%

A predictive model to estimate survival of hospitalized COVID-19 patients from admission data

Levy

Richardson

Coppa

et al. 2020

Preprint

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Objective: Our primary objective was to use initial data available to clinicians to characterize and predict survival for hospitalized coronavirus disease 2019 patients. While clinical characteristics and mortality risk factors of COVID-19 patients have been reported, a practical survival calculator based on data from a diverse group of U.S. patients has not yet been introduced. Such a tool would provide timely and valuable guidance in decision-making during this global pandemic.Design: We extracted demographic, laboratory, clinical, and treatment data from electronic health records and used it to build and test the predictive accuracy of a survival probability calculator referred to as "the Northwell COVID-19 Survival ('NOCOS') calculator."Setting: 13 acute care facilities at Northwell Health served as the setting for this study.Participants: 5,233 hospitalized COVID-19-positive patients served as the participants for this study.Main outcome measures: The NOCOS calculator was constructed using multivariate regression with L1 regularization (LASSO) to predict survival during hospitalization. Model predictive performance was measured using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC) of the calculators.Results: Patient age, serum blood urea nitrogen, Emergency Severity Index, red cell distribution width, absolute neutrophil count, serum bicarbonate, and glucose were identified as the optimal predictors of survival by multivariate LASSO regression. The predictive performance of the NOCOS calculator had an AUC of 0.832, reaching 0.91 when updated for each patient daily, with stability assessed and maintained for 14 consecutive days. This outperformed other established models, including the Sequential Organ Failure Assessment (SOFA) score (0.732). Conclusions:We present a practical estimate of survival probability that outperforms other general risk models. The seven early predictors of in-hospital survival can help clinicians identify patients with increased probabilities of survival and provide critical decision support as COVID-19 spreads across the U.S. Trial registration: N/AAll rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Section: Discussionmentioning

confidence: 99%

A predictive model to estimate survival of hospitalized COVID-19 patients from admission data

Levy

Richardson

Coppa

et al. 2020

Preprint

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“…In a paper about the role of artificial intelligence in COVID pandemic, Santosh KC et al report that it could be useful to identify COVID-19 outbreaks and forecast their nature of spread across the globe [15]. The application of artificial intelligence to chest CT is reported by Li et al on a retrospective and multicenter study of 4356 chest CT examinations, applying a deep learning model, COVID-19 detection neural network (COVNet).…”

Section: Sirm Recommendations On the Use Of Artificial Intelligence Imentioning

confidence: 99%

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

et al. 2020

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The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients.(1) SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. (2) SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. (3) Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.

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“…In recent days, numerous research articles published on epidemic prediction of the corona-virus pandemic [20][21][22][23][24][25][26][27][28][29]. Researchers focused to designed new paradigm based on AI-driven tools [30,31] combining ML algorithms and different modality of data. An improved adaptive neuro-fuzzy inference system (ANFIS) methodology is proposed in [32].…”

Section: (B) Researches On Covid-19mentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

Pal¹,

Sekh²,

Kar³

et al. 2020

Preprint

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The recent worldwide outbreak of the novel corona-virus (COVID-19) opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow Long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimized and automatically design country-specific networks. We have combined the trend data and weather data together for the prediction. The results show that the proposed pipeline outperforms against state-of-the-art methods for 170 countries data and can be a useful tool for such risk categorization. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

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AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

Cited by 322 publications

References 17 publications

A predictive model to estimate survival of hospitalized COVID-19 patients from admission data

A predictive model to estimate survival of hospitalized COVID-19 patients from admission data

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

Neural Network Based Country Wise Risk Prediction of COVID-19

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