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BackgroundWhereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response.Methods and ResultsThe Swedish Heart Failure Registry is a nationwide registry collecting detailed demographic, clinical, laboratory, and medication data and linked to databases with outcome information. We applied random forest modeling to identify predictors of 1‐year survival. Cluster analysis was performed and validated using serial bootstrapping. Association between clusters and survival was assessed with Cox proportional hazards modeling and interaction testing was performed to assess for heterogeneity in response to HF pharmacotherapy across propensity‐matched clusters. Our study included 44 886 HF patients enrolled in the Swedish Heart Failure Registry between 2000 and 2012. Random forest modeling demonstrated excellent calibration and discrimination for survival (C‐statistic=0.83) whereas left ventricular ejection fraction did not (C‐statistic=0.52): there were no meaningful differences per strata of left ventricular ejection fraction (1‐year survival: 80%, 81%, 83%, and 84%). Cluster analysis using the 8 highest predictive variables identified 4 clinically relevant subgroups of HF with marked differences in 1‐year survival. There were significant interactions between propensity‐matched clusters (across age, sex, and left ventricular ejection fraction and the following medications: diuretics, angiotensin‐converting enzyme inhibitors, β‐blockers, and nitrates, P<0.001, all).ConclusionsMachine learning algorithms accurately predicted outcomes in a large data set of HF patients. Cluster analysis identified 4 distinct phenotypes that differed significantly in outcomes and in response to therapeutics. Use of these novel analytic approaches has the potential to enhance effectiveness of current therapies and transform future HF clinical trials.

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Pavement distresses lead to pavement deterioration and failure. Accurate identification and classification of distresses helps agencies evaluate the condition of their pavement infrastructure and assists in decision-making processes on pavement maintenance and rehabilitation. The state of the art is automated pavement distress detection using vision-based methods. This study implements two deep learning techniques, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLO) v3, for automated distress detection and classification of high resolution (1,800 × 1,200) three-dimensional (3D) asphalt and concrete pavement images. The training and validation dataset contained 625 images that included distresses manually annotated with bounding boxes representing the location and types of distresses and 798 no-distress images. Data augmentation was performed to enable more balanced representation of class labels and prevent overfitting. YOLO and Faster R-CNN achieved 89.8% and 89.6% accuracy respectively. Precision-recall curves were used to determine the average precision (AP), which is the area under the precision-recall curve. The AP values for YOLO and Faster R-CNN were 90.2% and 89.2% respectively, indicating strong performance for both models. Receiver operating characteristic (ROC) curves were also developed to determine the area under the curve, and the resulting area under the curve values of 0.96 for YOLO and 0.95 for Faster R-CNN also indicate robust performance. Finally, the models were evaluated by developing confusion matrices comparing our proposed model with manual quality assurance and quality control (QA/QC) results performed on automated pavement data. A very high level of match to manual QA/QC, namely 97.6% for YOLO and 96.9% for Faster R-CNN, suggest the proposed methodology has potential as a replacement for manual QA/QC.

Big data technology is applied to analyze massive micro-seismic data set, which incorporates previously over-looked data set. This, in turn, will give redundant fracture modelling in stages and exact fracture propagation map in real time. Micro-seismic is pivotal to the success of Hydraulic Fracturing activity. However, with the advent of advanced geophones, the massive dataset requires a different analytical point of view, currently absent in conventional database processing and algorithms. HADOOP equips with the necessary tools for better and advanced real-time processing and analysis. Various Algorithms are developed to show comprehensive fracture operation analysis. Previous job failures are used to predict future anomalies, hence enhancing success ratio. The holistic dataset for the reservoir (Exploration, Drilling, and Production) are considered to synchronize the reservoir information. For example, drilling data (ROP, drillability, WOB etc.) is analyzed to predict the type of formation (like Brittle or Ductile), poroelastic constant, elasticity etc. Comprehensive analysis of fracture propagation would consider all the parameters associated not just conventional ones. The dataset is stored in Hadoop and called upon whenever needed. The massive amount of dataset is not being processed in conventional databases but can be integrated using Hadoop. The analytical results provided from Hadoop stands out from conventional formulae based software. The visualization of results keeps the minimum scope of error contradicting with the currently used ones. In current ones, many data trends and parameters are left out which are not used in formulae. Those patterns are visually shown and incorporated into the analysis, causing better mapping of fractures. Not only just complementing current analysis, Big Data provides the scope of comprehensive analysis from start to end. When 3D seismic appeared, it was a radical change. It not only showed 2D maps were of low resolution, rather those were rendered misleading. The Hadoop analytics is providing a unique perspective, leaving some mismatches, which are needed and to be seriously considered for future planning. The resulting model does not use conventional formulae, hence not limited to consider the real-time data. Rather field data (associated with noise) is analyzed using algorithms, generating trends from noises and deviations. The conventional software misses massive relevant data, which apparently cannot be incorporated into formulae. That inability is being met with Big Data analytics. Conventional database management is unable to handle so much data, which are being taken care off by Hadoop platform.