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Background: The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice. Methods: In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data. Results: We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival. Conclusion: In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment. [ABSTRACT FROM AUTHOR]

This paper aims to improve trust models in multi-agent systems based on four vital components, namely: reliability, similarity, satisfaction and trust transitivity. A number of different methods of computing these components were analyzed by considering the most representative existing trust models. The four trust components were identified from existing models then a trust model named trust transitivity-satisfaction-similarity-reliability (TtSSR) was proposed based on these components. TtSSR applied fuzzy logic for computing the identified components. Then by integrating the identified components and using Technique for Order of Preference by Similarity to Ideal Solution as a decision-making method, TtSSR selects the most trustworthy provider agent. The performance of TtSSR was compared with existing trust models using a simulator, specifically with Bayesian Network Model, Probability Certainty Distribution Model, and Dynamic Trust Model which are based on probability and TREPPS which is based on fuzzy logic. The experimental results revealed that TtSSR can significantly improve the accuracy of trust models; while the result of simulations demonstrated that the average accuracy of TtSSR in selecting a trustworthy agent is better than other models. Generally, the results indicated that when these four components were integrated, they performed significantly better in selecting a trustworthy agent as compared to other models. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced trust model for evaluating and selecting trustworthy advisor agents in multi-agent systems. In particular, the study proposes a computational trust model based on three main components, reliability, reputation and risk of interactions (3-R) in order to select trustworthy advisor agents. These advisors are buyer agents which suggest a trustworthy seller agent for transaction. The 3-R model represents a method for calculating reliability, with each advisor that has similar preferences with the buyer. Experimental results are presented in defense of the proposed approach and to demonstrate the accuracy of the presented model in evaluating the trustworthiness of advisors, even when a social network of advisors contains few numbers of advisors. The proposed 3-R model will be useful for researchers to improve the safety of electronic marketplaces in the case where the buyer agent has no previous experience with sellers, and it has to trust the other buyers as an advisor to make a decision about sellers. [ABSTRACT FROM AUTHOR]

BACKGROUND: Considering the conflicting results and limited studies on the association between elevated liver enzyme levels and COVID-19 outcomes, in the present study, we aimed to investigate the association between hepatic enzyme changes and the prognosis of COVID-19 during hospital admission. METHODS: In this prospective study, 1017 consecutive patients with COVID-19 participated and were followed up from admission until they were discharged or deceased. The liver enzyme levels were recorded on admission. The patient/disease-related information was recorded by trained nurses using questionnaires. The primary endpoint was the association between elevated liver enzymes and liver injury and mortality from COVID. RESULTS: The mean age of the participants was 62.58 ± 17.45 years; 55.4% of them were male. There was no significant difference between groups regarding the COVID-19 outcomes except for the need for ICU admission (P = 0.02). Moreover, all COVID-19 outcomes were significantly higher in patients with liver injury compared with other patients except for the quick sequential organ failure assessment (qSOFA) score. After adjusting for covariates, the patients with Alanine aminotransferase (ALT) and Aspartate aminotransferase (AST) levels of more than 40 (IU/L) and participants with liver injury on admission had significantly greater odds of death, ICU admission, and mechanical ventilation requirements. CONCLUSION: The results of the present study support the hypothesis that poor outcomes of COVID-19 infection were higher in patients with elevated liver enzyme levels and liver injury. Therefore, liver chemicals should be closely monitored during the illness and hospital admission, and patients with COVID-19 and an elevated level of transaminases should be followed up carefully, and necessary interventions should be considered to prevent poor outcomes. [ABSTRACT FROM AUTHOR]

Background: There are limited number of studies with controversial findings regarding the association between anemia at admission and coronavirus disease 2019 (COVID-19) outcomes. Therefore, in this research, we aimed to investigate the prospective association between anemia and COVID-19 outcomes in hospitalized patients in Iran.Methods: In this prospective study, the data of 1274 consecutive patients hospitalized due to COVID-19 were statistically analyzed. All biomarkers, including hemoglobin and high-sensitivity C-reactive protein (hs-CRP) levels were measured using standard methods. Anemia was defined as a hemoglobin (Hb) concentration of less than 13 g/dL and 12 g/dL in males and females, respectively. Assessing the association between anemia and COVID-19 survival in hospitalized patients was our primary endpoint.Results: The mean age of the participants was 64.43 ± 17.16 years, out of whom 615 (48.27%) were anemic subjects. Patients with anemia were significantly older (P = 0.02) and had a higher frequency of cardiovascular diseases, hypertension, kidney disease, diabetes, and cancer (P < 0.05). The frequency of death (anemic: 23.9% vs. nonanemic: 13.8%), ICU admission (anemic: 27.8% vs. nonanemic:14.71%), and ventilator requirement (anemic: 35.93% vs. nonanemic: 20.63%) were significantly higher in anemic patients than in nonanemic patients (P < 0.001). According to the results of regression analysis, after adjusting for significant covariate in the univariable model, anemia was independently associated with mortality (OR: 1.68, 95% CI: 1.10, 2.57, P = 0.01), ventilator requirement (OR: 1.74, 95% CI: 1.19, 2.54, P = 0.004), and the risk of ICU admission (OR: 2.06, 95% CI: 1.46, 2.90, P < 0.001).Conclusion: The prevalence of anemia in hospitalized patients with COVID-19 was high and was associated with poor outcomes of COVID-19. [ABSTRACT FROM AUTHOR]