Early Prediction of Patient Mortality Based on Routine Laboratory Tests and Predictive Models in Critically Ill Patients

We propose a method for quantitative analysis of predictive power of laboratory tests and early detection of mortality risk by usage of predictive models and feature selection techniques. Our method allows automatic feature selection, model selection, and evaluation of predictive models. Experimental evaluation was conducted on patients with renal failure admitted to ICUs (medical intensive care, surgical intensive care, cardiac, and cardiac surgery recovery units) at Boston’s Beth Israel Deaconess Medical Center. Data are extracted from Multi parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database. We built and evaluated different single (e.g. Logistic regression) and ensemble (e.g. Random Forest) learning methods. Results revealed high predictive accuracy (area under the precision-recall curve (AUPRC) values >86%) from day four, with acceptable results on the second (>81%) and third day (>85%). Random forests seem to provide the best predictive accuracy. Feature selection techniques Gini and ReliefF scored best in most cases. Lactate, white blood cells, sodium, anion gap, chloride, bicarbonate, creatinine, urea nitrogen, potassium, glucose, INR, hemoglobin, phosphate, total bilirubin, and base excess were most predictive for hospital mortality. Ensemble learning methods are able to predict hospital mortality with high accuracy, based on laboratory tests and provide ranking in predictive priority

Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities

In this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and deployment of ubiquitous systems for assessment and prediction of early risks of elderly Mild Cognitive Impairments (MCI) and frailty, and for supporting generation and delivery of optimal personalized preventive interventions that mitigate those risks, utilizing smart city datasets and IoT infrastructure. Low level data collected from IoT devices are preprocessed as sequences of activities, with temporal and causal variations in sequences classified as normal or anomalous behavior. The goals of proposed methodology are to (1) recognize significant behavioral variation patterns and (2) support early identification of pattern changes. Temporal clustering models are applied in detection and prediction of the following variation types: intra-activity (single activity, single citizen) and inter-activity (multiple-activities, single citizen). Identified behavioral variations and anomalies are further mapped to MCI/frailty onset behavior and risk factors, following the developed geriatric expert model

IDPpi:Protein-protein interaction analyses of human intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency. © 2018 The Author(s)

Redesign of the cooperation concept in wood processing

Cooperation, in combination with specialization, is aimed at the better utilization of resources, shorter production cycle and cost reduction. This means that cooperation and specialization, in addition to technological aspect, also include the organization aspect. The problem in wood processing is the fact that the organization aspect is completely neglected. For this reason, specialization and cooperation cannot reach their goal. The aim of this paper is to point out the significance of the organization aspect. The selection of cooperants cannot be based on the prices and acquaintances, recommendations, etc. There are numerous criteria which can decide the selection of cooperants. The criteria adopted in this paper are the manufacturing price and the storage cost. Based on the multicriteria decision making, it is possible to define the alternative which enables simultaneously the lowest total price and the lowest total own storage costs

Flexibility of production systems and prepare-finish time

One of the specificities of the large-serial and mass production is the almost neglected percentage of prepare-finish time in the production cycle. In the conditions of today dominant discontinuous production, it becomes a significant element of the production cycle. The eastern (Japan) doctrine of increasing the flexibility of the production systems, is based inter alia also on the extreme reduction of the prepare-finish time. For this reason, the aim of this study was to identify the types and percentages of individual jobs within the group of prepare-finish jobs. The sample consisted of 3 (three) production systems for the production of joinery, with the discontinuous production system. The research shows that the percentage of time of the jobs installation of work instruments, regulation of processing regime, and removal of work instruments is extremely long and that it ranges between 11.83% and 18.93% of the shift time. The reasons of the high percentage of these jobs are the wide range of products and the absence of the rationalisation of prepare-finish jobs. Within the efforts to minimize the effects of disruption and to increase the flexibility of the production systems, the rationalisation of prepare-finish jobs is the unavoidable condition

Extending the Modelling Capacity of Gaussian Conditional Random Fields while Learning Faster

Gaussian Conditional Random Fields (GCRF) are atype of structured regression model that incorporatesmultiple predictors and multiple graphs. This isachieved by defining quadratic term feature functions inGaussian canonical form which makes the conditionallog-likelihood function convex and hence allows findingthe optimal parameters by learning from data. In thiswork, the parameter space for the GCRF model is extendedto facilitate joint modelling of positive and negativeinfluences. This is achieved by restricting the modelto a single graph and formulating linear bounds on convexitywith respect to the models parameters. In addition,our formulation for the model using one networkallows calculating gradients much faster than alternativeimplementations. Lastly, we extend the model onestep farther and incorporate a bias term into our linkweight. This bias is solved as part of the convex optimization.Benefits of the proposed model in terms ofimproved accuracy and speed are characterized on severalsynthetic graphs with 2 million links as well as on ahospital admissions prediction task represented as a humandisease-symptom similarity network correspondingto more than 35 million hospitalization records inCalifornia over 9 years