A machine learning based exploration of COVID-19 mortality risk

Mahdavi, Mahdi; Choubdar, Hadi; Zabeh, Erfan; Rieder, Michael J.; Safavi‐Naeini, Safieddin; Jobbágy, Zsolt; Ghorbani, Amirata; Abedini, Atefeh; Kiani, Arda; Khanlarzadeh, Vida; Lashgari, Reza; Kamrani, Ehsan

doi:10.1371/journal.pone.0252384

Cited by 71 publications

(46 citation statements)

References 39 publications

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“…The variables included (demographics, comorbidities, home medication, vital signs) are readily available in the ED. The laboratory values which are not always obtainable in ED were not included in the final model, which seemed to be feasible as described in a recent ML model published in the literature [ 34 ]. The variables associated with a significantly higher risk for COVID-19 severity in our model were male gender, older age, other as race, increased temperature, increased respiratory rate, decreased oxygen saturation, inflammatory bowel disease.…”

Section: Discussionmentioning

confidence: 99%

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

Lupei

Ingraham

et al. 2022

PLoS ONE

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Objective To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). Methods We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict “severe” COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. Results The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed “severe” COVID-19. Patients in the highest quintile developed “severe” COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). Conclusion A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

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Section: Discussionmentioning

confidence: 99%

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

Lupei

Ingraham

et al. 2022

PLoS ONE

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“…COVID-19 prediction and patient mortality prediction have been made on many specific biomarkers and blood tests [18][19][20]. However, our focus in this study is to provide an instant and easy method to give an early result for both cases.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

Monjur

Preo

Shams

et al. 2021

BioMed

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The coronavirus pandemic overwhelmed many countries and their healthcare systems. Shortage of testing kits and Intensive-Care-Unit (ICU) beds for critical patients have become a norm in most developing countries. This has prompted the need to rapidly identify the COVID-19 patients to stop the spread of the virus and also to find critical patients. The latter is imperative for determining the state of critically ill patients as quickly as possible. This will lower the number of deaths from the infection. In this paper, we propose a cloud-based smartphone application for the early prognosis of COVID-19 infected patients and also predict their mortality risk using their symptoms. Moreover, we heuristically identified the most important symptoms necessary for making such predictions. We have successfully reduced the number of features by almost half for the prognosis and by more than a third for forecasting the mortality risk, compared to the contemporary studies. The application makes the real-time analysis using machine learning models, designed and stored in the cloud. Our machine learning model demonstrates an accuracy, precision, recall, and F1 score of 97.72%, 100%, 95.55%, and 97.70%, respectively, in identifying the COVID-19 infected patients and with an accuracy, precision, recall, and F1 score of 90.83%, 88.47%, 92.94%, and 90.65%, respectively, in forecasting the mortality risk from the infection. The real-time cloud-based approach yields faster responses, which is critical in the time of pandemic for mitigating the infection spread and aiding in the efficient management of the limited ICU resources.

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“…Machine learning has become a popular and reliable analytical technique in recent years, especially in the medical domain. Many studies investigated hospitalised patients and ICU patients, for monitoring or mortality prediction [1][2][3][4][5][6][7][8]. Some of these studies investigated to what extent vital signs could inform on a patient's clinical deterioration and adverse events [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Some of these studies investigated to what extent vital signs could inform on a patient's clinical deterioration and adverse events [1,2]. Other studies focused on the mortality prediction of ICU patients and the relevant predictors [3,4,6]. For instance, in our previous study [4], we investigated the mortality prediction of ICU patients based on a set of vital signs, namely, heart rate, blood pressure, respiration rate, and oxygen saturation.…”

Section: Introductionmentioning

confidence: 99%

“…The main concept in that study was to engineer interpretable/simple features in combination with a linear hard margin support vector machine that can inform mortality prediction for ICU patients one to two days in advance. Moreover, in the study by Mahdavi et al [6], they developed support vector machine models to predict the mortality risk of COVID-19 patients, based on demographic and laboratory variables/features obtained from a patient's first day of admission. Another related approach involves vital sign predictors for hospitalised patients [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Vital Signs Prediction for COVID-19 Patients in ICU

Amer

Wouters

Vranken

et al. 2021

Sensors

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This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.

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A machine learning based exploration of COVID-19 mortality risk

Cited by 71 publications

References 39 publications

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

Vital Signs Prediction for COVID-19 Patients in ICU

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