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Intensive care unit (ICU) patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist but lack in specificity. This study focuses on two target groups, namely patients with thrombosis or cancer. The main goal is to develop and validate interpretable machine learning (ML) models to predict early and late mortality, while exploiting all available data stored in the medical record. To this end, retrospective data from two freely accessible databases, MIMIC-III and eICU, were used. Well-established ML algorithms were implemented utilizing automated and purposely built ML frameworks for addressing class imbalance. Prediction of early mortality showed excellent performance in both disease categories, in terms of the area under the receiver operating characteristic curve (AUC–ROC): VTE-MIMIC-III 0.93, eICU 0.87, cancer-MIMIC-III 0.94. On the other hand, late mortality prediction showed lower performance, i.e., AUC–ROC: VTE 0.82, cancer 0.74–0.88. The predictive model of early mortality developed from 1651 VTE patients (MIMIC-III) ended up with a signature of 35 features and was externally validated in 2659 patients from the eICU dataset. Our model outperformed traditional scoring systems in predicting early as well as late mortality. Novel biomarkers, such as red cell distribution width, were identified.

Background The prevalence of thrombophilic traits, which might further enhance the risk of thrombotic complications in children treated for cancer, varies significantly among different populations. Objective To evaluate the prevalence of common thrombophilic traits of the East Mediterranean Region, among native Cretan children treated for malignancy. Methods Blood samples were consecutively collected from 31 native Cretan children treated for acute lymphoblastic leukaemia (n = 19) or other malignancies (n = 12) over 3 years. A molecular diagnosis based on the presence of Factor V Leiden (FVL), as well as on PT G20210A and MTHFR C677T mutation (in 14 patients) using PCR was applied. Patients who had central venous catheters (n = 29) were treated with an intensified thromboprophylaxis protocol that had been previously established in our institution. Results The prevalence of the FVL mutation was 19.4% (95% CI = 5–32). The allele frequency is estimated at 11.3% (95% CI: 3.5–19.1) which is higher than that reported for the population of the mainland of Greece. The prevalence of the PT G20210A and MTHFR C677T mutation was 14.3 and 71.4%, respectively (corresponding allele frequencies 7.1 and 50%, respectively). Only one patient developed thrombosis, having although no thrombophilic trait. Conclusions Thrombophilic traits were relatively common in this group of native Cretan children treated for malignancy. Thromboprophylaxis should be considered in Cretan children in the presence of known acquired risk factors for thrombosis, but a larger prospective to study is first needed. © 2004 Wiley-Liss, Inc.

Stroke in an infant heterozygous carrier of both Factor V G1691A and the G20210A prothrombin mutation

Biomedical research often relies on having access to vast amounts of sensitive information. Patient data in electronic form are held in medical databases and bio-repositories and have to be queried, data mined and operated on by doctors and researchers. Lately, all this information has been migrating to the cloud making access easier for all interested parties. While this helps with dissemination and access, it may have unintended consequences in terms of security and privacy. In this work we propose an architecture that combines distributed access control mechanisms with privacy preserving cryptographic protocols to enable secure sharing and computations on clouds holding sensitive biomedical data. The data shared are tagged with security policies that define who has access to it and how they should be used. Access rights may be delegated to other parties making collaborations easier. Finally, data can be operated on cryptographically to extract specific information without compromising the entire data set.

Background Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation. Results A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance. Conclusion Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.