Context-aware Mixture of Deep Neural Networks

Alpha-beta network is a mixture of deep neural networks, implementing a mixture of experts, where each component is a neural network. It is trained using the expectation-maximization algorithm. It enables context-awareness as each component is pushed to give context-specific predictions. Such structure enables context uncertainty quantification as well. The effectiveness of alpha-beta network was assessed using two real-world activity datasets: UCI OPPORTUNITY and an in-house dataset. The model has shown superior performance compared to the baselines

BoXHED2.0: Scalable boosting of dynamic survival analysis

Modern applications of survival analysis increasingly involve time-dependent covariates. In healthcare settings, such covariates provide dynamic patient histories that can be used to assess health risks in realtime by tracking the hazard function. Hazard learning is thus particularly useful in healthcare analytics, and the open-source package BoXHED 1.0 provides the first implementation of a gradient boosted hazard estimator that is fully nonparametric. This paper introduces BoXHED 2.0, a quantum leap over BoXHED 1.0 in several ways. Crucially, BoXHED 2.0 can deal with survival data that goes far beyond right-censoring and it also supports recurring events. To our knowledge, this is the only nonparametric machine learning implementation that is able to do so. Another major improvement is that BoXHED 2.0 is orders of magnitude more scalable, due in part to a novel data preprocessing step that sidesteps the need for explicit quadrature when dealing with time-dependent covariates. BoXHED 2.0 supports the use of GPUs and multicore CPUs, and is available from GitHub: www.github.com/BoXHED.Comment: 12 page

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Complete genome sequence of Trueperella pyogenes strain Arash114, isolated from the uterus of a water buffalo (Bubalus bubalis) in Iran

Objective: Trueperella pyogenes has been considered a major causative agent of metritis, abortion, and death in a broad range of domestic and wild animals, including cattle, swine, sheep, goats, camels, buffalo, deer, antelopes, reptiles, and birds. Data description: Here, we report the complete chromosome sequence of Trueperella pyogenes strain Arash114, isolated from the uterus of a water buffalo (Bubalus bubalis) died due to the infection caused by this pathogen. The genome assembly comprised 2,338,282 bp, with a 59.5% GC content. Annotation of the genome showed 46 tRNA genes, 6 rRNA, 1 CRISPR and 2059 coding sequences. Also, several genes coding for antimicrobial resistance such as tetW and virulence factor including plo, nanH, nanP, cbp and 4 fimbrial proteins were found. This study will advance our knowledge regarding the metabolism, virulence factors, antibiotic resistance and evolution of Arash114 strain and serve as an appropriate template for future researches. Keywords: Complete genome sequencing; Trueperella pyogenes; Uterus infection; Water buffalo

Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset

Background Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi‐institutional cohort of patients with COVID‐19 using a DL‐based model. Purpose This study aimed to evaluate the performance of deep privacy‐preserving federated learning (DPFL) in predicting COVID‐19 outcomes using chest CT images. Methods After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold‐out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold‐out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. Results The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79–0.85) and (95% CI: 0.77–0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models ( p ‐value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. Conclusion The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi‐institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.</p

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p

The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% 47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% 32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% 27.9-42.8] and 33.3% 25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license