A new fuzzy set merging technique using inclusion-based fuzzy clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets

Addressing health information privacy with a novel cloud-based PHR system architecture

Patient Health Records (PHRs) shift the ownership of health data from health providers to patients. Such a shift poses important challenges from the data privacy point of view. Patients would like to be able to selectively reveal information to other stakeholders and, at the same time, be assured that their health information will not be used improperly once shared. Current PHR systems partially fail to satisfy these requirements. In this paper, we show that both requirements can be satisfied fully when adopting a novel cloud-based PHR system architecture.We expain the role of remote virtual machines in this architecture and use interaction models to reason about privacy implications. Finally, we evaluate MyPHRMachines, a prototypical implementation of the architecture: we demonstrate that the system enables the execution of third party genome analysis services on patientowned genome data while ensuring that (1) such services cannot maliciously store this data and (2) patients can show the analysis results to experts without sharing along their full genome

Tracebook : a dynamic checklist support system

It has recently been demonstrated that checklist scan enable significant improvements to patient safety. However, their clinical acceptance is significantly lower than expected. This is due to the lack of good support systems. Specifically, support systems are too static: this holds for paper-based support as well as for electronic systems that digitize paper-based support naively. Both approaches are independent from clinical process and clinical context. In this paper, we propose a process-oriented and context-aware dynamic checklist support system: Tracebook. This system supports the execution of complex clinical processes and rules involving data from Electronic Medical Record systems. Workflow activities and forms are specific to individual patients based on clinical rules and they are dispatched to the right user automatically based on a process model. Besides describing the Tracebook functionality in general, this paper demonstrates the support system specifically on an example application that we are preparing for a controlled clinical evaluation. At last we discuss the difference between Tracebook and other support systems which also rely on a checklist format

DCCSS:a meta-model for dynamic clinical checklist support systems

Clinical safety checklists receive much research attention since they can reduce medical errors and improve patient safety. Computerized checklist support systems are also being developed actively. Such systems should individualize checklists based on information from the patient’s medical record while also considering the context of the clinical workflows. Unfortunately, the form definitions, database queries and workflow definitions related to dynamic checklists are too often hard-coded in the source code of the support systems. This increases the cognitive effort for the clinical stakeholders in the design process, it complicates the sharing of dynamic checklist definitions as well as the interoperability with other information systems. In this paper, we address these issues by contributing the DCCSS meta-model which enables the model-based development of dynamic checklist support systems. DCCSS was designed as an incremental extension of standard meta-models, which enables the reuse of generic model editors in a novel setting. In particular, DCCSS integrates the Business Process Model and Notation (BPMN) and the Guideline Interchange Format (GLIF), which represent best of breed languages for clinical workflow modeling and clinical rule modeling respectively. We also demonstrate one of the use cases where DCCSS has already been applied in a clinical setting

Measurement and physical interpretation of the mean motion of turbulent density patterns detected by the BES system on MAST

The mean motion of turbulent patterns detected by a two-dimensional (2D) beam emission spectroscopy (BES) diagnostic on the Mega Amp Spherical Tokamak (MAST) is determined using a cross-correlation time delay (CCTD) method. Statistical reliability of the method is studied by means of synthetic data analysis. The experimental measurements on MAST indicate that the apparent mean poloidal motion of the turbulent density patterns in the lab frame arises because the longest correlation direction of the patterns (parallel to the local background magnetic fields) is not parallel to the direction of the fastest mean plasma flows (usually toroidal when strong neutral beam injection is present). The experimental measurements are consistent with the mean motion of plasma being toroidal. The sum of all other contributions (mean poloidal plasma flow, phase velocity of the density patterns in the plasma frame, non-linear effects, etc.) to the apparent mean poloidal velocity of the density patterns is found to be negligible. These results hold in all investigated L-mode, H-mode and internal transport barrier (ITB) discharges. The one exception is a high-poloidal-beta (the ratio of the plasma pressure to the poloidal magnetic field energy density) discharge, where a large magnetic island exists. In this case BES detects very little motion. This effect is currently theoretically unexplained.Comment: 28 pages, 15 figures, submitted to PPC

Landslide Risk Assessment by Using a New Combination Model Based on a Fuzzy Inference System Method

Landslides are one of the most dangerous phenomena that pose widespread damage to property and human lives. Over the recent decades, a large number of models have been developed for landslide risk assessment to prevent the natural hazards. These models provide a systematic approach to assess the risk value of a typical landslide. However, often models only utilize the numerical data to formulate a problem of landslide risk assessment and neglect the valuable information provided by experts’ opinion. This leads to an inherent uncertainty in the process of modelling. On the other hand, fuzzy inference systems are among the most powerful techniques in handling the inherent uncertainty. This paper develops a powerful model based on fuzzy inference system that uses both numerical data and subjective information to formulate the landslide risk more reliable and accurate. The results show that the proposed model is capable of assessing the landslide risk index. Likewise, the performance of the proposed model is better in comparison with that of the conventional techniques

Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P = .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

A replication study of JTC bias, genetic liability for psychosis and delusional ideation

Background This study attempted to replicate whether a bias in probabilistic reasoning, or ‘jumping to conclusions’(JTC) bias is associated with being a sibling of a patient with schizophrenia spectrum disorder; and if so, whether this association is contingent on subthreshold delusional ideation. Methods Data were derived from the EUGEI project, a 25-centre, 15-country effort to study psychosis spectrum disorder. The current analyses included 1261 patients with schizophrenia spectrum disorder, 1282 siblings of patients and 1525 healthy comparison subjects, recruited in Spain (five centres), Turkey (three centres) and Serbia (one centre). The beads task was used to assess JTC bias. Lifetime experience of delusional ideation and hallucinatory experiences was assessed using the Community Assessment of Psychic Experiences. General cognitive abilities were taken into account in the analyses. Results JTC bias was positively associated not only with patient status but also with sibling status [adjusted relative risk (aRR) ratio : 4.23 CI 95% 3.46–5.17 for siblings and aRR: 5.07 CI 95% 4.13–6.23 for patients]. The association between JTC bias and sibling status was stronger in those with higher levels of delusional ideation (aRR interaction in siblings: 3.77 CI 95% 1.67–8.51, and in patients: 2.15 CI 95% 0.94–4.92). The association between JTC bias and sibling status was not stronger in those with higher levels of hallucinatory experiences. Conclusions These findings replicate earlier findings that JTC bias is associated with familial liability for psychosis and that this is contingent on the degree of delusional ideation but not hallucination

A replication study of JTC bias, genetic liability for psychosis and delusional ideation

Background This study attempted to replicate whether a bias in probabilistic reasoning, or ‘jumping to conclusions’(JTC) bias is associated with being a sibling of a patient with schizophrenia spectrum disorder; and if so, whether this association is contingent on subthreshold delusional ideation. Methods Data were derived from the EUGEI project, a 25-centre, 15-country effort to study psychosis spectrum disorder. The current analyses included 1261 patients with schizophrenia spectrum disorder, 1282 siblings of patients and 1525 healthy comparison subjects, recruited in Spain (five centres), Turkey (three centres) and Serbia (one centre). The beads task was used to assess JTC bias. Lifetime experience of delusional ideation and hallucinatory experiences was assessed using the Community Assessment of Psychic Experiences. General cognitive abilities were taken into account in the analyses. Results JTC bias was positively associated not only with patient status but also with sibling status [adjusted relative risk (aRR) ratio : 4.23 CI 95% 3.46–5.17 for siblings and aRR: 5.07 CI 95% 4.13–6.23 for patients]. The association between JTC bias and sibling status was stronger in those with higher levels of delusional ideation (aRR interaction in siblings: 3.77 CI 95% 1.67–8.51, and in patients: 2.15 CI 95% 0.94–4.92). The association between JTC bias and sibling status was not stronger in those with higher levels of hallucinatory experiences. Conclusions These findings replicate earlier findings that JTC bias is associated with familial liability for psychosis and that this is contingent on the degree of delusional ideation but not hallucination