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

Autoencoder techniques for survival analysis on renal cell carcinoma

Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of survival (like genetic alterations or transcriptomic patterns of gene expression) is one of the most relevant fields in current research. Statistical methods and metrics to analyze time-to-event data are crucial in understanding disease progression and the effectiveness of treatments. However, in the medical field, data is often high-dimensional, complicating the application of such methodologies. In this study, we addressed this challenge by compressing the high-dimensional transcriptomic data of patients treated with immunotherapy (avelumab + axitinib) and a TKI (sunitinib) into latent, meaningful features using autoencoders. We applied a semi-parametric statistical approach based on the COX Proportional Hazards model, coupled with Breslow's estimator, to predict each patient's Progression-Free Survival (PFS) and determine survival functions. Our analysis explored various penalty configurations and their combinations. Given the complexity of transcriptomic data, we extended our model to incorporate both tabular data and its graph variant, where edges represent protein-protein interactions between genes, offering a more insightful approach. Recognizing the interpretability challenges inherent in neural networks, particularly autoencoders, we analyzed the mutual information between genes in the original data and their latent feature representations to clarify which genes are most associated with specific latent variables. The results indicate that different types of autoencoders are better suited for different tasks: denoising autoencoders excel at accurate reconstruction, while the sparse variant is more effective at producing meaningful representations. Additionally, combining these penalties enhances both reconstruction quality and the interpretability of latent features. The interpretable models identified genes such as LRP2 and ACE2 as highly relevant to renal cell carcinoma. This research underscores the utility of autoencoders in managing high-dimensional data problems.

Prediction models based on machine learning algorithms for COVID-19 severity risk

BACKGROUND

The World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X; therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar diseases.

METHODS

Prediction models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. These models were evaluated for prediction accuracy, area under the curve (AUC), sensitivity, and specificity as well as were interpreted using SHapley Additive exPlanation.

RESULTS

Data were collected from 1,485 hospitalized patients across 6 centers, comprising 1,184 patients with severe or critical COVID-19 and 301 patients with nonsevere COVID-19. Among the four models, the SVM model achieved the highest prediction accuracy of 98.45%, with an AUC of 0.994, a sensitivity of 0.989, and a specificity of 0.969. Moreover, oxygenation index (OI), confusion, respiratory rate, and age were found to be predictors of COVID-19 severity risk.

CONCLUSIONS

SVM could accurately predict COVID-19 severity risk; thus, it can be prioritized as a prediction model. OI is the most critical predictor of COVID-19 severity risk and can serve as the primary and independent evaluation indicator.

Predicting higher risk factors for COVID-19 short-term reinfection in patients with rheumatic diseases: a modeling study based on XGBoost algorithm

BACKGROUND

Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.

METHODS

We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases. Psychological health was evaluated using the Functional Assessment of Chronic Illness Therapy Fatigue (FACIT-F) scale, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder 7-item (GAD-7) questionnaire, and the Pittsburgh Sleep Quality Index (PSQI) scale. Health status and disease activity were assessed using the EuroQol-5 Dimension-3 Level (EQ-5D-3L) descriptive system and the Visual Analogue Score (VAS) scale. The model performance was assessed by Area Under the Receiver Operating Characteristic Curve (AUC), Area Under the Precision-Recall Curve (AUPRC), and the geometric mean of sensitivity and specificity (G-mean). SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of each predictor to the model outcomes.

RESULTS

The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance with an AUC of 0.91 (95% CI 0.87-0.95). Significant factors of short-term reinfection included glucocorticoid taper (OR = 2.61, 95% CI 1.38-4.92), conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) taper (OR = 2.97, 95% CI 1.90-4.64), the number of symptoms (OR = 1.24, 95% CI 1.08-1.42), and GAD-7 scores (OR = 1.07, 95% CI 1.02-1.13). FACIT-F scores were associated with a lower likelihood of short-term reinfection (OR = 0.95, 95% CI 0.93-0.96). Besides, we found that the GAD-7 score was one of the most important predictors.

CONCLUSION

We developed explainable machine learning models to predict the risk of short-term COVID-19 reinfection in patients with rheumatic diseases. SHAP analysis highlighted the importance of clinical and psychological factors. Factors included anxiety, fatigue, depression, poor sleep quality, high disease activity during initial infection, and the use of glucocorticoid taper were significant predictors. These findings underscore the need for targeted preventive measures in this patient population.

Identification of Risk Factors for Relapse in Childhood Leukemia Using Penalized Semi-parametric Mixture Cure Competing Risks Model

BACKGROUND

Leukemia is the most common childhood malignancy. Identifying prognostic factors of patient survival and relapse using more reliable statistical models instead of traditional variable selection methods such as stepwise regression is of great importance. The present study aimed to apply a penalized semi-parametric mixture cure model to identify the prognostic factors affecting short-term and long-term survival of childhood leukemia in the presence of competing risks. The outcome of interest in this study was time to relapse. Study Design: A retrospective cohort study.

METHODS

A total of 178 patients (0‒15 years old) with leukemia participated in this study (September 1997 to September 2016, followed up to June 2021) at Golestan University of Medical Sciences, Iran. Demographic, clinical, and laboratory data were collected, and then a penalized semi-parametric mixture cure competing risk model with smoothly clipped absolute deviation (SCAD) and least absolute shrinkage and selection operator (LASSO) regularizations was used to analyze the data.

RESULTS

Important prognostic factors of relapse patients selected by the SCAD regularization method were platelets (150000‒400000 vs.>400000; odds ratio=0.31) in the cure part and type of leukemia (ALL vs. AML, hazard ratio (HR)=0.08), mediastinal tumor (yes vs. no, HR=16.28), splenomegaly (yes vs. no; HR=2.94), in the latency part. In addition, significant prognostic factors of death identified by the SCAD regularization method included white blood cells (<4000 vs.>11000, HR=0.25) and rheumatoid arthritis signs (yes vs. no, HR=5.75) in the latency part.

CONCLUSION

Several laboratory factors and clinical side effects were associated with relapse and death, which can be beneficial in treating the disease and predicting relapse and death time.

Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

INTRODUCTION

Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes.

METHODS

We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance.

RESULTS

Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators.

CONCLUSION

Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.

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

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.

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

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.

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

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 (E_max) 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 E_max 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) E_max 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.

Comparison the effects and side effects of Covid-19 vaccination in patients with inflammatory bowel disease (IBD): a systematic scoping review

Covid-19 is a pandemic disease that is more severe and mortal in people with immunodeficiency, such as those with inflammatory bowel disease (IBD). On the other hand, no definitive treatment has been identified for it and the best way to control it is wide spread vaccination. The aim of this study was to evaluate the benefits and side effects of different vaccines in patients with IBD. Three Electronic databases [Medline (accessed from PubMed), Scopus, Science Direct, and Cochrane] were searched systematically without time limit, using MESH terms and the related keywords in English language. We focused on the research studies on the effect and side effects of Covid-19 vaccination in patients with IBD. Articles were excluded if they were not relevant, or were performed on other patients excerpt patients with IBD. Considering the titles and abstracts, unrelated studies were excluded. The full texts of the remained studies were evaluated by authors, independently. Then, the studies' findings were assessed and reported. Finally, after reading the full text of the remained articles, 15 ones included in data extraction. All included studied were research study, and most of them (12/15) had prospective design. Totally, 8/15 studies were performed in single-center settings. In 8/15 studies, patients with IBD were compared with a control group. The results were summarized the in two categories: (1) the effect of vaccination, and (2) side effects. The effect of vaccination were assessed in 13/15 studies. Side effects of Covid-19 vaccination in patients with IBD were reported in 7/15 studies. Patients with IBD can be advised that vaccination may have limited minor side effects, but it can protect them from the serious complications of Covid-19 and its resulting mortality with a high success rate. They should be also mentioned in booster doses.

The prognostic effect of clinical and laboratory findings on in-hospital mortality in patients with confirmed COVID-19 disease

Background:

COVID-19 is known as a global health issue, which can cause high morbidity and mortality in patients. It is necessary to identify biomarkers, clinical and laboratory findings and effects on patients' mortality.

Objective:

This study aimed to evaluate the prognostic effect of clinical and laboratory findings on in-hospital mortality in patients with confirmed COVID-19.

Methods:

This retrospective cross-sectional study (February-August 2020) was conducted on adult patients with COVID-19, who were hospitalized in one of the main reference hospitals affiliated to Shiraz University of Medical Sciences, southern Iran. Patients with uncompleted or missed medical files were excluded from the study. Clinical and laboratory findings were extracted from the patients' medical files and then analyzed. The patients were categorized as survivor and nonsurvivors groups, and they were compared.

Results:

Totally, 345 patients were enrolled that 205 (59.4%) were male. The mean±SD of age was 53.67±16.97 years, and 32 (9.3%) were died. Hypertension (28.4%) and diabetes (25.5%) were the most prevalent comorbidities. All clinical symptoms were similar in both groups, except fever, which was observed significantly more in nonsurvivors (P=0.027). The duration of hospitalization was 9.20±5.62 (range; 2-42) days, which was higher in nonsurvivors (P<0.001). The results of Multivariate Logistic Regression Model showed that CRP (OR=1.032, P=0.01) and INR (OR=48.88, P=0.049) were the predictor factors for in-hospital mortality in hospitalized patients with confirmed COVID-19.

Conclusion:

The current study showed that in-hospital mortality was obtained as 9.3%. It was found that CRP and INR were the predictor factors for in-hospital mortality in hospitalized patients with confirmed COVID-19.