A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction

Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition

The Royal Free Hospital score: a calibrated prognostic model for patients with cirrhosis admitted to intensive care unit. Comparison with current models and CLIF-SOFA score

Prognosis for patients with cirrhosis admitted to intensive care unit (ICU) is poor. ICU prognostic models are more accurate than liver-specific models. We identified predictors of mortality, developed a novel prognostic score (Royal Free Hospital (RFH) score), and tested it against established prognostic models and the yet unvalidated Chronic Liver Failure-Sequential Organ Failure Assessment (CLIF-SOFA) model

Implementation of a novel online condition monitoring thermal prognostic indicator system

This research aims to develop a reliable and robust online condition monitoring thermal prognostic indicator system which will reduce the risk of failures in a Power System Network. Real-time measurements (weather conditions, temperature of the cable joints or terminations, loading demand) taken close to underground cable will update the prognostic simulation model. Anomalies of the measurements along the cable will be compared with the predicted ones hence indicating a possible degradation activity in the cable. The use of such systems within a power networks will provide a smarter way of prognostic condition monitoring in which you measure less and model more. The use of suggested thermal models will enable the power network operators to maximize asset utilization and minimize constraint costs in the system

Prognostic value of routine laboratory variables in prediction of breast cancer recurrence.

The prognostic value of routine laboratory variables in breast cancer has been largely overlooked. Based on laboratory tests commonly performed in clinical practice, we aimed to develop a new model to predict disease free survival (DFS) after surgical removal of primary breast cancer. In a cohort of 1,596 breast cancer patients, we analyzed the associations of 33 laboratory variables with patient DFS. Based on 3 significant laboratory variables (hemoglobin, alkaline phosphatase, and international normalized ratio), together with important demographic and clinical variables, we developed a prognostic model, achieving the area under the curve of 0.79. We categorized patients into 3 risk groups according to the prognostic index developed from the final model. Compared with the patients in the low-risk group, those in the medium- and high-risk group had a significantly increased risk of recurrence with a hazard ratio (HR) of 1.75 (95% confidence interval [CI] 1.30-2.38) and 4.66 (95% CI 3.54-6.14), respectively. The results from the training set were validated in the testing set. Overall, our prognostic model incorporating readily available routine laboratory tests is powerful in identifying breast cancer patients who are at high risk of recurrence. Further study is warranted to validate its clinical application

Survival prediction in mesothelioma using a scalable lasso regression model: instructions for use and initial performance using clinical predictors

Introduction: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models. Methods: Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS <6 months and OS <12 months. OS prediction was quantified using Somers DXY statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores. Results: Median OS was 270 (IQR 140–450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean DXY0.332 (±0.019)). However, validation set DXY was only 0.221 (0.0935–0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The <6-month OS model demonstrated 74% sensitivity and 68% specificity. The <12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies. Conclusions: The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging

Economic Growth in Georgia: Historical Perspectives and Prognosis

While output declined in virtually all transition economies in the initial years, the speed and extent of the recovery that followed has varied widely across these countries. The paper examines some aspects of transition experiences of 1990s and dynamics of GDP in Georgia during transition recession and following post-recession recovery. Economic growth is considered as complex and comprehensive phenomenon. The prognostic econometric model of Georgian GDP is developed.economic growth, GDP, transitional economy, Georgia, prognostic model, regression, non –linear trend

Echocardiography combined with cardiopulmonary exercise testing for the prediction of outcome in idiopathic pulmonary arterial hypertension

BACKGROUND: Right ventricular (RV) function is a major determinant of exercise intolerance and outcome in idiopathic pulmonary arterial hypertension (IPAH). The aim of the study was to evaluate the incremental prognostic value of echocardiography of the RV and cardiopulmonary exercise testing (CPET) on long-term prognosis in these patients. METHODS: One hundred-thirty treatment-naïve IPAH patients were enrolled and prospectively followed. Clinical worsening (CW) was defined by a reduction in 6-minute walk distance plus an increase in functional class, or non elective hospitalization for PAH, or death. Baseline evaluation included clinical, hemodynamic, echocardiographic and CPET variables. Cox regression modeling with c-statistic and bootstrapping validation methods were done. RESULTS: During a mean period of 528 ± 304 days, 54 patients experienced CW (53%). Among demographic, clinical and hemodynamic variables at catheterization, functional class and cardiac index were independent predictors of CW (Model-1). With addition of echocardiographic and CPET variables (Model-2), peak O2 pulse (peak VO2/heart rate) and RV fractional area change (RVFAC) independently improved the power of the prognostic model (AUC: 0.81 vs 0.66, respectively; p=0.005). Patients with low RVFAC and low O2 pulse (low RVFAC + low O2 pulse) and high RVFAC+low O2 pulse showed 99.8 and 29.4 increase in the hazard ratio, respectively (relative risk -RR- of 41.1 and 25.3, respectively), compared with high RVFAC+high O2 pulse (p=0.0001). CONCLUSIONS: Echocardiography combined with CPET provides relevant clinical and prognostic information. A combination of low RVFAC and low O2 pulse identifies patients at a particularly high risk of clinical deterioration