Prognostic machine learning models for COVID‐19 to facilitate decision making

Subudhi, Sonu; Verma, Ashish; Patel, Ankit

doi:10.1111/ijcp.13685

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…These tools have also been applied to outpatients to predict outcomes such as heart failure progression 9 . Machine learning tools can also be applied to predict outcomes such as Intensive Care Unit (ICU) admission and mortality 10 . Thus far there have been few studies that examined specific machine learning algorithms in predicting outcomes such as ICU admission/mortality in COVID-19 patients [11][12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

et al. 2021

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As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.

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Section: Introductionmentioning

confidence: 99%

Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

et al. 2021

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“…Prediction is the second popular objective in AI/ML approaches [ 106 – 111 ]. Regression analysis is a widely accepted model for prediction purposes (100% of studies) [ 112 ].…”

Section: Resultsmentioning

confidence: 99%

A systematic review on AI/ML approaches against COVID-19 outbreak

Doğan

Tiwari

Jabbar³

et al. 2021

Complex Intell. Syst.

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A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

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“…To aid the decision-learning model, Wiley et al (2020) proposed a predictive ML technique for COVID-19. The system will identify high-risk people in early-stage identification and allocate required services (e.g., ICU beds) and some necessary treatments before permanent clinical harm occurs [39].…”

Section: ) Clinical Decision-making Toolsmentioning

confidence: 99%

Development of Digitalization Road Map for Healthcare Facility Management

et al. 2022

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Effective Healthcare Facility Management (HFM) remain a crucial concern for high quality built healthcare sectors, both in the public and private areas. The anticipated resource efficiencies, complex systems, and maintenance are all driving up demand for efficiency and flexibility. However, it has been almost impossible with limited human and machinery resources to cope with the healthcare sector's demands. The added value of Digitization will assist organizations overcoming the limited resources issue to deal with the increasing healthcare demands. Based on an extensive literature review, the aim of this paper is to suggest innovative digital and facility management methods that improve the healthcare sector experience. The same literature has been used to extract significant variables to build a causal loop diagram (CLD) that assists in better understanding the interrelationship between those variables within healthcare systems. Furthermore, to prepare for the technology adoption, the paper conducted a cross-sectional survey to forecast the acceptance level in 550 participants from Qatar's healthcare sector. It also discusses applying the most innovative healthcare artificial intelligence techniques through an organized digital transformation process to guarantee delivering the healthcare services efficiently while optimizing resources and costs.

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Prognostic machine learning models for COVID‐19 to facilitate decision making

Abstract: An increasing number of COVID-19 cases worldwide has overwhelmed the healthcare system. Physicians are struggling to allocate resources and to focus their attention on high-risk patients, partly because early identification of high-risk individuals is difficult. This can be attributed to

Cited by 17 publications

References 13 publications

Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

A systematic review on AI/ML approaches against COVID-19 outbreak

Development of Digitalization Road Map for Healthcare Facility Management

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