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Knowledge base construction (KBC) using AI has been one of the key goals of this highly popular technology since its emergence, as it helps to comprehend everything, including relations, around us. The construction of knowledge bases can summarize a piece of text in a machine-processable and understandable way. This can prove to be valuable and assistive to knowledge engineers. In this paper, we present the application of natural language processing in the construction of knowledge bases. We demonstrate how a trained bidirectional long short-term memory or bi-LSTM neural network model can be used to construct knowledge bases in accordance with the exact ISO26262 definitions as defined in the GENIAL! Basic Ontology. We provide the system with an electronic text document from the microelectronics domain and the system attempts to create a knowledge base from the available information in textual format. This information is then expressed in the form of graphs when queried by the user. This method of information retrieval presents the user with a much more technical and comprehensive understanding of an expert piece of text. This is achieved by applying the process of named entity recognition (NER) for knowledge extraction. This paper provides a result report of the current status of our knowledge construction process and knowledge base content, as well as describes our challenges and experiences.

Abstract We investigated the prevalence of germline BRCA mutations in a population‐based cohort of Austrian women diagnosed with ovarian cancer and its association with family history of cancer. We prospectively collected family pedigrees of 443 Austrian ovarian cancer patients who had been tested for the presence of a germline BRCA or 2 mutations and correlated the familial breast and ovarian cancer burden with the prevalence of BRCA mutations and disease onset. The probability of carrying a gBRCA mutation in patients without family history of cancer is 14% (95% CI 9%‐22%), as opposed to 45% (95% CI 31%‐59%) of patients with at least one family member with ovarian cancer, and 47% (95% CI 40%‐54%) if other relatives have developed breast cancer. If both breast and ovarian cancer are diagnosed in the family, the probability of carrying a germline BRCA1 or 2 mutations is 60% (95% CI 50%‐68%). germline BRCA1 or mutations in families with ovarian cancer only are commonly located in the Ovarian Cancer Cluster Regions when compared to families with both breast and ovarian cancer (P = 0.001, and P = 0.020, respectively). While gBRCA mutation carriers with ovarian cancer do not have a significantly different age at onset than patients with a family history of cancer, gBRCA1 carriers in general have an earlier onset than gBRCA2 carriers (P = 0.002) and patients without a mutation (P = 0.006). The rate of germline BRCA1 or 2 mutations in ovarian cancer patients without a family history or breast or ovarian cancer is low. However, in women with additional family members affected, the prevalence is considerably higher than previously reported.

Despite increasing clinical interest in adapting checkpoint inhibitor (CPI) therapies for patients with gynecologic malignancies, no accurate clinical biomarkers to predict therapy response and prognosis are currently available. Therefore, we aimed to assess the predictive and prognostic value of pretherapeutic body mass index (BMI) for recurrent gynecologic cancer patients as previously validated for other solid tumors. We evaluated patients with programmed cell death ligand 1 (PD-L1) positive and, in endometrial cancer, also mismatch repair deficient (MMR) gynecologic malignancies, who received the PD-1 inhibitor pembrolizumab as monotherapy (200 mg fixed-dose q3 w) from 2017 to 2020 (n = 48). Thirty-six patients receiving at least four courses were included in the final analysis. Associations between a BMI increase per 5 kg/m2 and overall response rate (ORR; complete + partial response), disease control rate (DCR; ORR + stable disease), progression-free (PFS), and overall survival (OS) were assessed. An elevated BMI was univariately associated with ORR (OR 10.93 [CI 2.39–49.82], p = 0.002), DCR (OR 2.19 [CI 0.99–4.83], p = 0.048), prolonged PFS (HR 1.54 [CI 1.03–2.34], p = 0.038), and OS (HR 1.87 [CI 1.07–3.29], p = 0.028). All results could be confirmed in the multivariate analyses. Pretherapeutic BMI therefore appears to be a promising readily available biomarker to identify patients with PD-L1-positive and/or MMR-deficient gynecologic malignancies who could particularly benefit from CPI treatment.

Cyber-Physical Systems (CPS) pose new challenges to verification and validation that go beyond the proof of functional correctness based on high-level models. Particular challenges are, in particular for formal methods, its heterogeneity and scalability. For numerical simulation, uncertain behavior can hardly be covered in a comprehensive way which motivates the use of symbolic methods. The paper describes an approach for symbolic simulation-based verification of CPS with uncertainties. We define a symbolic model and representation of uncertain computations: Affine Arithmetic Decision Diagrams. Then we integrate this approach in the SystemC AMS simulator that supports simulation in different models of computation. We demonstrate the approach by analyzing a water-level monitor with uncertainties, self-diagnosis, and error-reactions.

To develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.We performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA) was then used to evaluate the clinical net benefit of the prediction model.Ovarian cancer including borderline tumours was found in 241 (26.6%) patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87-0.94). DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.We confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.

IoT systems consist of Hardware/Software systems (e.g., sensors) that are embedded in a physical world, networked and that interact with complex software platforms. The validation of such systems is a challenge and currently mostly done by prototypes. This paper presents the virtual environment for simulation, emulation and validation of an IoT platform and its semantic model in real life scenarios. It is based on a decentralized, bottom up approach that offers interoperability of IoT devices and the value-added services they want to use across different domains. The framework is demonstrated by a comprehensive case study. The example consists of the complete IoT “Smart Energy” use case with focus on data privacy by homomorphic encryption. The performance of the network is compared while using partially homomorphic encryption, fully homomorphic encryption and no encryption at all.As a major result, we found that our framework is capable of simulating big IoT networks and the overhead introduced by homomorphic encryption is feasible for VICINITY.

OBJECTIVES:To investigate fused multiparametric positron emission tomography/magnetic resonance imaging (MP PET/MRI) at 3T in patients with locally advanced cervical cancer, using high-resolution T2-weighted, contrast-enhanced MRI (CE-MRI), diffusion-weighted imaging (DWI), and the radiotracers [18F]fluorodeoxyglucose ([18F]FDG) and [18F]fluoromisonidazol ([18F]FMISO) for the non-invasive detection of tumor heterogeneity for an improved planning of chemo-radiation therapy (CRT). MATERIALS AND METHODS:Sixteen patients with locally advanced cervix were enrolled in this IRB approved and were examined with fused MP [18F]FDG/ [18F]FMISO PET/MRI and in eleven patients complete data sets were acquired. MP PET/MRI was assessed for tumor volume, enhancement (EH)-kinetics, diffusivity, and [18F]FDG/ [18F]FMISO-avidity. Descriptive statistics and voxel-by-voxel analysis of MRI and PET parameters were performed. Correlations were assessed using multiple correlation analysis. RESULTS:All tumors displayed imaging parameters concordant with cervix cancer, i.e. type II/III EH-kinetics, restricted diffusivity (median ADC 0.80x10-3mm2/sec), [18F]FDG- (median SUVmax16.2) and [18F]FMISO-avidity (median SUVmax3.1). In all patients, [18F]FMISO PET identified the hypoxic tumor subvolume, which was independent of tumor volume. A voxel-by-voxel analysis revealed only weak correlations between the MRI and PET parameters (0.05-0.22), indicating that each individual parameter yields independent information and the presence of tumor heterogeneity. CONCLUSION:MP [18F]FDG/ [18F]FMISO PET/MRI in patients with cervical cancer facilitates the acquisition of independent predictive and prognostic imaging parameters. MP [18F]FDG/ [18F]FMISO PET/MRI enables insights into tumor biology on multiple levels and provides information on tumor heterogeneity, which has the potential to improve the planning of CRT.

Gamma-glutamyltransferase (GGT) is a known marker for apoptotic balance and cell detoxification. Recently, an association of baseline GGT levels and breast cancer incidence, tumor progression and chemotherapy resistance was shown. The purpose of this study was to evaluate the association of pre-therapeutic GGT levels, clinical-pathological parameters and survival in patients with primary metastatic breast cancer (PMBC).In this multicenter analysis, pre-therapeutic GGT levels and clinical-pathological parameters of 114 patients diagnosed with PMBC between 1996 and 2012 were evaluated. The association between GGT levels and clinical-pathological parameters were analysed. Patients were stratified into four GGT risk-groups (GGT < 18.00 U/L: normal low, 18.00 to 35.99 U/L: normal high, 36.00 to 71.99 U/L: elevated and ≥ 72.00 U/L: highly elevated) and survival analyses were performed.Patients in the high risk GGT group had a poorer overall survival, when compared to the low risk group with five-year overall survival rates of 39.5% and 53.7% (p = 0.04), respectively. Patients with larger breast tumors had a trend towards higher GGT levels (p = 0.053). Pre-therapeutic GGT levels were not associated with indicators of aggressive tumor biology such as HER2-status, triple negative histology, or poorly differentiated cancers.Pre-therapeutic GGT serum level might serve as a novel prognostic factor for overall-survival in patients with PMBC.

OBJECTIVE:Tumor cachexia is an important prognostic parameter in epithelial ovarian cancer (EOC). Tumor cachexia is characterized by metabolic and inflammatory disturbances. These conditions might be reflected by body composition measurements (BCMs) ascertained by pre-operative computed tomography (CT). Thus, we aimed to identify the prognostically most relevant BCMs assessed by pre-operative CT in EOC patients. METHODS:We evaluated muscle BCMs and well established markers of nutritional and inflammatory status, as well as clinical-pathological parameters in 140 consecutive patients with EOC. Furthermore, a multiplexed inflammatory marker panel of 25 cytokines was used to determine the relationship of BCMs with inflammatory markers and patient's outcome. All relevant parameters were evaluated in uni- and multivariate survival analysis. RESULTS:Muscle attenuation (MA)-a well established BCM parameter-is an independent prognostic factor for survival in multivariate analysis (HR 2.25; p = 0.028). Low MA-reflecting a state of cachexia-is also associated with residual tumor after cytoreductive surgery (p = 0.046) and with an unfavorable performance status (p = 0.015). Moreover, MA is associated with Eotaxin and IL-10 out of the 25 cytokine multiplex marker panel in multivariate linear regression analysis (p = 0.021 and p = 0.047, respectively). CONCLUSION:MA-ascertained by routine pre-operative CT-is an independent prognostic parameter in EOC patients. Low MA is associated with the inflammatory, as well as the nutritional component of cachexia. Therefore, the clinical value of pre-operative CT could be enhanced by the assessment of MA.

C-reactive protein (CRP) has previously been shown to serve as a prognostic parameter in women with gynecologic malignancies. Due to the lack of valid prognostic markers for uterine leiomyosarcoma (ULMS) this study set out to investigate the value of pre-treatment CRP serum levels as prognostic parameter.Data of women with ULMS were extracted from databases of three Austrian centres for gynaecologic oncology. Pre-treatment CRP serum levels were measured and correlated with clinico-pathological parameters. Univariate and multivariable survival analyses were performed.In total, 53 patients with ULMS were included into the analysis. Mean (SD) CRP serum level was 3.46 mg/dL (3.96). Solely, an association between pre-treatment CRP serum levels and tumor size (p = 0.04) but no other clinic-pathologic parameter such as tumor stage (p = 0.16), or histological grade (p = 0.07), was observed. Univariate and multivariable survival analyses revealed that CRP serum levels (HR 2.7 [1.1-7.2], p = 0.037) and tumor stage (HR 6.1 [1.9-19.5], p = 0.002) were the only independent prognostic factors for overall survival (OS) in patients with ULMS. Patients with high pre-treatment CRP serum levels showed impaired OS compared to women with low levels (5-year-OS rates: 22.6% and 52.3%, p = 0.007).High pre-treatment CRP serum levels were independently associated with impaired prognosis in women with ULMS and might serve as a prognostic parameter in these patients.

AIMS: While it is commonly accepted that health care costs have been rising to unprecedented levels, the question remains whether the increased expenditure actually affords increased health outcomes. It was the objective of this study to search for associations between health care spending and health care outcome, after adjusting for potential confounding variables, using aggregate data collected since the introduction of diagnosis-related groups (DRG) into Austrian health care financing in 1997. METHODS: Two parameters of health care outcome, mortality and years of life lost (YLL), were regressed on direct and indirect measures of health care spending. We used ordinary least squares, Prais-Winsten, and 2-stage least squares regression in model building to account for autocorrelation and endogeneity. RESULTS: Our findings showed that health care spending was associated with mortality and YLL reduction. The strongest association among the independent variables was seen for spending for prevention. The strongest association for the dependent variables was seen for cardiovascular disease followed by injuries. Also, socio-economic status (SES) was shown to be an important confounder in all studied associations. Our data suggest that increases in health care spending produce significant increases in health. CONCLUSION: Health care spending should not be constrained, but instead an optimised resource allocation would afford an increase in health per expenditure. Emphasising spending in prevention and reduction of SES gradients would strengthen this association.