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Background & Aim: Timely identification of the patients requiring intensive care unit admission (ICU) could be life‐saving. We aimed to compare different machine learning algorithms to predict the requirements for ICU admission in COVID‐19 patients. Methods: We screened all patients with COVID‐19 at six academic hospitals in Tehran comprising our study population. A total of 44,112 COVID‐19 patients (≥18 years old) were included, among which 7722 patients were hospitalized. We used a Random Forest algorithm to select significant variables. Then, prediction models were developed using the Support Vector Machine, Naıve Bayes, logistic regression, lightGBM, decision tree, and K‐Nearest Neighbor algorithms. Sensitivity, specificity, accuracy, F1 score, and receiver operating characteristic‐Area Under the Curve (AUC) were used to compare the prediction performance of different models. Results: Based on random Forest, the following predictors were selected: age, cardiac disease, cough, hypertension, diabetes, influenza & pneumonia, malignancy, and nervous system disease. Age was found to have the strongest association with ICU admission among COVID‐19 patients. All six models achieved an AUC greater than 0.60. Naıve Bayes achieved the best predictive performance (AUC = 0.71). Conclusion: Naïve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID‐19 patients. Machine learning models could help quickly identify high‐risk patients upon entry and reduce mortality and morbidity among COVID‐19 patients. [ABSTRACT FROM AUTHOR]

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Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug–protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug–protein interaction significantly improves the prediction of synergistic drug combinations. Availability and implementation The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy. [ABSTRACT FROM AUTHOR]

Background: Decompensated cirrhosis patients have a high risk of death which can be considerably reduced with liver transplantation (LT). This study aimed to simultaneously investigate the effect of some patients' characteristics on mortality among those with/without LT and also LT incident. Materials and Methods: In this historical cohort study, the information from 780 eligible patients aged 18 years or older was analyzed by the Markov multistate model; they had been listed between 2008 and 2014, needed a single organ for initial orthotopic LT, and followed at least for up to 5 years. Results: With a median survival time of 6 (5-8) years, there were 275 (35%) deaths. From 255 (33%) patients who had LT, 55 (21%) subsequently died. Factors associated with a higher risk of mortality and LT occurrence were included: higher model for end-stage liver disease (MELD) score (hazard ratio [HR] = 1.16, confidence interval [CI]: 1.09-1.24 and HR = 1.22, CI: 1.41-1.30) and ascites complication (HR = 2.34, CI: 1.74-3.16 and HR = 11.43, CI: 8.64-15.12). Older age (HR = 1.03, CI: 1.01-1.06), higher creatinine (HR = 6.87, CI: 1.45-32.56), and autoimmune disease versus hepatitis (HR = 2.53, CI: 1.12-5.73) were associated with increased risk of mortality after LT. Conclusion: The MELD and ascites are influential factors on waiting list mortality and occurrence of LT. Total life expectancy is not influenced by higher MELD. [ABSTRACT FROM AUTHOR]

Cox model and traditional frailty models assume that all individuals will eventually experience the event of interest. This assumption is often overlooked, and situations will arise where it is not realistic. We introduce Compound Poisson frailty model for survival analysis to deal with populations in which some of the individuals will not experience the event of interest. This model assumes that the target population is a mixture of individuals with zero frailty and those with positive frailty. In this paper, we consider a compound Poisson frailty model for right-censored event times from a Bayesian perspective and compute the Bayesian estimator using the Markov Chain Monte Carlo method, where a Gamma process prior is adopted for the baseline hazard function. Furthermore, we evaluate the approach using simulation studies and demonstrate the methodology by analyzing the data from achalasia patient cohort. [ABSTRACT FROM AUTHOR]