Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm

Ebrahimi, Vahid; Sharifi, Mehrdad; Mousavi‐Roknabadi, Razieh Sadat; Sadegh, Robab; Khademian, Mohammad Hossein; Moghadami, Mohsen; Dehbozorgi, Afsaneh

doi:10.1186/s12889-021-12383-3

Cited by 11 publications

(6 citation statements)

References 43 publications

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“…Using the pre-selected features as predictors, we performed Elastic Net regularization. The process that we used has two tuning parameters: the regularization parameter lambda and the mixing parameter alpha for moderating between Lasso and Ridge [ 29 ]. The optimal model should specify alpha and lambda, for which the two repeated 5-fold cross-validated penalized log-likelihood deviance is minimal after comparing all the training datasets from 40 imputed datasets.…”

Section: Methodsmentioning

confidence: 99%

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Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

Aguayo

Zhang

Vaillant

et al. 2023

BMC Med Res Methodol

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Background In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population. Methods We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016–2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson’s, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf). Results 5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno’s C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk. Conclusion TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.

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Section: Methodsmentioning

confidence: 99%

“…The process that we used has two tuning parameters: the regularization parameter lambda and the mixing parameter alpha for moderating between Lasso and Ridge [29].…”

Section: Cox Models With Elastic Net Regularization (Coxen)mentioning

confidence: 99%

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

Aguayo

Zhang

Vaillant

et al. 2023

BMC Med Res Methodol

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“…Zeng et al reported developing a machine-learning model to predict severe chronic obstructive pulmonary disease exacerbations: a retrospective cohort study ( 48 ). Ebrahimi et al reported predictive determinants of overall survival among re-infected patients with COVID-19 using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm ( 49 ). Together, these studies further supported the feasibility of machine-learning approaches to problem solving.…”

Section: Discussionmentioning

confidence: 99%

A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome

Wang

Li²,

Mao³

et al. 2022

Front. Nutr.

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The present study aimed to explore the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predict an appropriate dosage schedule using a machine-learning approach. Data were obtained from literature mining and the rates of body weight change from the initial values were selected as the therapeutic index. The maximal effect (Emax) model was built up as the machine-learning model. A total of 242 patients with PCOS were included for analysis. In the machine-learning model, the Emax of carnitine supplementation on body weight was −3.92%, the ET50 was 3.6 weeks, and the treatment times to realize 25%, 50%, 75%, and 80% (plateau) Emax of carnitine supplementation on body weight were 1.2, 3.6, 10.8, and 14.4 weeks, respectively. In addition, no significant relationship of dose-response was found in the dosage range of carnitine supplementation used in the present study, indicating the lower limit of carnitine supplementation dosage, 250 mg/day, could be used as a suitable dosage. The present study first explored the effect of carnitine supplementation on body weight in patients with PCOS, and in order to realize the optimal therapeutic effect, carnitine supplementation needs 250 mg/day for at least 14.4 weeks.

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“…Also, Ebrahimi et al 20 developed a ML model for predicting overall survival (OS) among reinfected COVID-19 patients. Two ML algorithms were applied – elastic-net regularised Cox-adjusted PH model and backward stepwise elimination – to a dataset of 283 reinfected COVID-19 patients admitted to 26 medical centres in Iran.…”

Section: Introductionmentioning

confidence: 99%

Detecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning

Daramola,

Kavu,

Kotze

et al. 2023

DIGITAL HEALTH

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Background COVID-19 vaccines offer different levels of immune protection but do not provide 100% protection. Vaccinated persons with pre-existing comorbidities may be at an increased risk of SARS-CoV-2 breakthrough infection or reinfection. The aim of this study is to identify the critical variables associated with a higher probability of SARS-CoV-2 breakthrough infection using machine learning. Methods A dataset comprising symptoms and feedback from 257 persons, of whom 203 were vaccinated and 54 unvaccinated, was used for the investigation. Three machine learning algorithms – Deep Multilayer Perceptron (Deep MLP), XGBoost, and Logistic Regression – were trained with the original (imbalanced) dataset and the balanced dataset created by using the Random Oversampling Technique (ROT), and the Synthetic Minority Oversampling Technique (SMOTE). We compared the performance of the classification algorithms when the features highly correlated with breakthrough infection were used and when all features in the dataset were used. Result The results show that when highly correlated features were considered as predictors, with Random Oversampling to address data imbalance, the XGBoost classifier has the best performance (F1 = 0.96; accuracy = 0.96; AUC = 0.98; G-Mean = 0.98; MCC = 0.88). The Deep MLP had the second best performance (F1 = 0.94; accuracy = 0.94; AUC = 0.92; G-Mean = 0.70; MCC = 0.42), while Logistic Regression had less accurate performance (F1 = 0.89; accuracy = 0.88; AUC = 0.89; G-Mean = 0.89; MCC = 0.68). We also used Shapley Additive Explanations (SHAP) to investigate the interpretability of the models. We found that body temperature, total cholesterol, glucose level, blood pressure, waist circumference, body weight, body mass index (BMI), haemoglobin level, and physical activity per week are the most critical variables indicating a higher risk of breakthrough infection. Conclusion These results, evident from our unique data source derived from apparently healthy volunteers with cardiovascular risk factors, follow the expected pattern of positive or negative correlations previously reported in the literature. This information strengthens the body of knowledge currently applied in public health guidelines and may also be used by medical practitioners in the future to reduce the risk of SARS-CoV-2 breakthrough infection.

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Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm

Cited by 11 publications

References 43 publications

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome

Detecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning

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