scholarly journals An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning

2021 ◽  
Author(s):  
Muhammad E. H. Chowdhury ◽  
Tawsifur Rahman ◽  
Amith Khandakar ◽  
Somaya Al-Madeed ◽  
Susu M. Zughaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
High Sensitivity ◽  
Risk Groups ◽  
Healthcare Services ◽  
Learning Tools ◽  
Reactive Protein ◽  
Disease Mortality ◽  
Death Probability ◽  
Severity Of The Disease

AbstractCOVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)—acquired at hospital admission—were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5–50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.

scholarly journals Early prediction of mortality risk among patients with severe COVID-19, using machine learning

2020 ◽  
Author(s):  
Chuanyu Hu ◽  
Zhenqiu Liu ◽  
Yanfeng Jiang ◽  
Oumin Shi ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Mortality Risk ◽  
Protein Level ◽  
Lymphocyte Count ◽  
External Validation ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
D Dimer

Abstract Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. Methods Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients’ outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models’ performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. Results The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. Conclusions Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.

scholarly journals Early prediction of mortality risk among severe COVID-19 patients using machine learning

2020 ◽  
Author(s):  
Chuanyu Hu ◽  
Zhenqiu Liu ◽  
Yanfeng Jiang ◽  
Xin Zhang ◽  
Oumin Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Predictive Model ◽  
Mortality Risk ◽  
Protein Level ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Probability Of Death ◽  
D Dimer

AbstractBackgroundCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been spreading globally. The number of deaths has increased with the increase in the number of infected patients. We aimed to develop a clinical model to predict the outcome of severe COVID-19 patients early.MethodsEpidemiological, clinical, and first laboratory findings after admission of 183 severe COVID-19 patients (115 survivors and 68 nonsurvivors) from the Sino-French New City Branch of Tongji Hospital were used to develop the predictive models. Five machine learning approaches (logistic regression, partial least squares regression, elastic net, random forest, and bagged flexible discriminant analysis) were used to select the features and predict the patients’ outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models’ performance. Sixty-four severe COVID-19 patients from the Optical Valley Branch of Tongji Hospital were used to externally validate the final predictive model.ResultsThe baseline characteristics and laboratory tests were significantly different between the survivors and nonsurvivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count, and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the derivation and external validation sets were 0.895 and 0.881, respectively. The sensitivity and specificity were 0.892 and 0.687 for the derivation set and 0.839 and 0.794 for the validation set, respectively, when using a probability of death of 50% as the cutoff. The individual risk score based on the four selected variables and the corresponding probability of death can serve as indexes to assess the mortality risk of COVID-19 patients. The predictive model is freely available at https://phenomics.fudan.edu.cn/risk_scores/.ConclusionsAge, high-sensitivity C-reactive protein level, lymphocyte count, and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

scholarly journals A Machine Learning Approach for the Diagnosis of Diabetes : A Review

2020 ◽  
pp. 509-516
Author(s):  
Pravin S. Rahate ◽  
Nikhat Raza
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Selection Process ◽  
High Sensitivity ◽  
Healthcare Services ◽  
Automated System ◽  
Retinal Images ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Patients With Diabetes

Diabetes mellitus (DM) is a chronic disease that affects 382 million patients’ worldwide (2013 data) and is predicted to increase to as many as 592 million adults by 2035. DM is one of the major causes of blindness in young adults around the world. The most serious ocular complication of DM is diabetic retinopathy (DR).Diabetic retinopathy is the most common microvascular complication in diabetes1, for the screening of which the retinal imaging is the most widely used method due to its high sensitivity in detecting retinopathy. Prompt diagnosis is important through efficient screening. The evaluation of the severity and degree of retinopathy associated with a person having diabetes is currently performed by medical experts based on the fundus or retinal images of the patient’s eyes As the number of patients with diabetes is rapidly increasing, the number of retinal images produced by the screening programmes will also increase, which in turn introduces a large labor-intensive burden on the medical experts as well as cost to the healthcare services. Manual grading of these images to determine the severity of DR is rather slow and resource demanding. This could be alleviated with an automated system either as support for medical experts’ work or as full diagnosis tool. This labor-intensive task could greatly benefit from automatic detection using machine learning technique. Early detection and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. The objective of this paper is to explore the work happening on the detection, progression and feature selection process for the prediction of DR and to establish the extent and depth of existing knowledge on RD prediction process.

scholarly journals Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1582
Author(s):  
Tawsifur Rahman ◽  
Fajer A. Al-Ishaq ◽  
Fatima S. Al-Mohannadi ◽  
Reem S. Mubarak ◽  
Maryam H. Al-Hitmi ◽  
...  
Keyword(s):  
High Mortality ◽  
Mortality Risk ◽  
External Validation ◽  
Model Development ◽  
Risk Groups ◽  
Mortality Prediction ◽  
Hospital Death ◽  
High Mortality Risk ◽  
Death Probability ◽  
Improve Patient Management

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

scholarly journals A machine learning-based model for survival prediction in patients with severe COVID-19 infection

2020 ◽  
Author(s):  
Li Yan ◽  
Hai-Tao Zhang ◽  
Jorge Goncalves ◽  
Yang Xiao ◽  
Maolin Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Severity ◽  
Medical Knowledge ◽  
Predictive Biomarkers ◽  
High Sensitivity ◽  
Lactic Dehydrogenase ◽  
Healthcare Services ◽  
Medical Attention ◽  
Survival Prediction ◽  
Sudden Increase

AbstractThe sudden increase of COVID-19 cases is putting a high pressure on healthcare services worldwide. At the current stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 404 infected patients in the region of Wuhan, China to identify crucial predictive biomarkers of disease severity. For this purpose, machine learning tools selected three biomarkers that predict the survival of individual patients with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this paper suggests a simple and operable formula to quickly predict patients at the highest risk, allowing them to be prioritised and potentially reducing the mortality rate.FundingNone.

scholarly journals Physical Activity, High-Sensitivity C-Reactive Protein, and Total and Cardiovascular Disease Mortality in Type 2 Diabetes

Diabetes Care ◽  
2011 ◽  
Vol 34 (7) ◽  
pp. 1492-1496 ◽  
Author(s):  
T. Vepsalainen ◽  
M. Soinio ◽  
J. Marniemi ◽  
S. Lehto ◽  
A. Juutilainen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Cardiovascular Disease Mortality ◽  
Reactive Protein ◽  
Disease Mortality
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