Machine learning to assist clinical decision-making during the COVID-19 pandemic

Debnath, Shubham; Barnaby, Douglas P.; Coppa, Kevin; Makhnevich, Alex; Kim, Young Ho; Chatterjee, Saurav; Tóth, Viktor; Levy, Todd; Paradis, Marc; Cohen, Stuart L.; Hirsch, Jamie S.; Zanos, Theodoros P.; Becker, Lance B.; Cookingham, Jennifer; Davidson, Karina W.; Dominello, Andrew J.; Falzon, Louise; McGinn, Thomas; Mogavero, Jazmin N.; Osorio, Gabrielle A.

doi:10.1186/s42234-020-00050-8

Cited by 75 publications

(52 citation statements)

References 58 publications

(32 reference statements)

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“…Despite the ethical concern regarding the application of AI with patients’ data in the current pandemic scenario, these methods should be used to support medical staff [27] , [28] , [29] . The stress factors that affect medical professionals during this pandemic scenario concerning the increase of patients in the hospitals are significantly affect their work and performance [30] . Consequently, it is necessary to create novel methods that can support their work.…”

Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

249

124

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COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors’ knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.

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Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

249

124

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“…Recent work has also shown that biomarkers, like heart rate and PPG amplitude, can be used to predict responses to transcutaneous cervical vagus nerve stimulation (tcVNS) and model dynamic characteristics of an adapting ANS (Gazi et al, 2020). While it is unclear how this may scale for other conditions or interventions, modeling biomarker responses can be applied to information to inform diagnostic and treatment actions (Wiens & Shenoy, 2018;Norgeot et al, 2019;Debnath et al 2020). Continuous data from many sensors, including those in this study and adding electroencephalography (EEG) or other neural recording devices, can be used to train such a model on recordings from healthy, able-bodied individuals to characterize ANS balance.…”

Section: Discussionmentioning

confidence: 99%

A method to quantify autonomic nervous system function in healthy, able-bodied individuals

Debnath

Levy

Bellehsen

et al. 2020

Preprint

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The autonomic nervous system (ANS), which maintains physiological homeostasis in various organ systems via parasympathetic and sympathetic branches, is altered in common diffuse and focal conditions. Sensitive, quantitative biomarkers could detect changes in ANS function, first here in healthy participants and eventually in patients displaying dysautonomia. This framework combines controlled autonomic testing with feature extraction from physiological responses. Twenty-one individuals were assessed in two morning and two afternoon sessions over two weeks. Each session included five standard clinical tests probing autonomic function: squat test, cold pressor test, diving reflex test, deep breathing, and Valsalva maneuver. Noninvasive sensors captured continuous electrocardiography, blood pressure, breathing, electrodermal activity, and pupil diameter. Heart rate, heart rate variability, mean arterial pressure, electrodermal activity, and pupil diameter responses to the perturbations were extracted, and averages across participants were computed. A template matching algorithm calculated scaling and stretching features that optimally fit the average to an individual response. These features were grouped based on test and modality to derive sympathetic and parasympathetic indices for this healthy population. A significant positive correlation (p = 0.000377) was found between sympathetic amplitude response and body mass index. Additionally, longer duration and larger amplitude sympathetic and longer duration parasympathetic responses occurred in afternoon testing sessions; larger amplitude parasympathetic responses occurred in morning sessions. These results demonstrate the robustness and sensitivity of an algorithmic approach to extract multimodal responses from standard tests. This novel method of quantifying ANS function can be used for early diagnosis, measurement of disease progression, or treatment evaluation.

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“…This system is then used to predict which patients require ICU admission. In [122] a machine learning algorithm is presented to assist clinical decision making during the pandemic. In [123] , different machine learning models including SVM, KNN, Decision Tree, Gaussian Naive Bayesian, etc.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

148

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Machine learning to assist clinical decision-making during the COVID-19 pandemic

Cited by 75 publications

References 58 publications

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

A method to quantify autonomic nervous system function in healthy, able-bodied individuals

Applications of artificial intelligence in battling against covid-19: A literature review

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