Michal Burda

Past and Present ARDS Mortality Rates: A Systematic Review

ARDS is severe form of respiratory failure with significant impact on the morbidity and mortality of critical care patients. Epidemiological data are crucial for evaluating the efficacy of therapeutic interventions, designing studies, and optimizing resource distribution. The goal of this review is to present general aspects of mortality data published over the past decades. A systematic search of the MEDLINE/PubMed was performed. The articles were divided according to their methodology, type of reported mortality, and time. The main outcome was mortality. Extracted data included study duration, number of patients, and number of centers. The mortality trends and current mortality were calculated for subgroups consisting of in-hospital, ICU, 28/30-d, and 60-d mortality over 3 time periods (A, before 1995; B, 1995–2000; C, after 2000). The retrospectivity and prospectivity were also taken into account. Moreover, we present the most recent mortality rates since 2010. One hundred seventy-seven articles were included in the final analysis. General mortality rates ranged from 11 to 87% in studies including subjects with ARDS of all etiologies (mixed group). Linear regression revealed that the study design (28/30-d or 60-d) significantly influenced the mortality rate. Reported mortality rates were higher in prospective studies, such as randomized controlled trials and prospective observational studies compared with retrospective observational studies. Mortality rates exhibited a linear decrease in relation to time period (P < .001). The number of centers showed a significant negative correlation with mortality rates. The prospective observational studies did not have consistently higher mortality rates compared with randomized controlled trials. The mortality trends over 3 time periods (before 1995, 1995–2000, and after 2000) yielded variable results in general ARDS populations. However, a mortality decrease was present mostly in prospective studies. Since 2010, the overall rates of in-hospital, ICU, and 28/30-d and 60-d mortality were 45, 38, 30, and 32%, respectively.

Linguistic fuzzy logic in R

The aim of this paper is to present a new package for the R statistical environment that enables the use of linguistic fuzzy logic in data processing applications. The lfl package provides tools for transformation of data into fuzzy sets representing linguistic expressions, for mining of linguistic fuzzy association rules, and for performing an inference on fuzzy rule bases using the Perception-based Logical Deduction (PbLD). The package also contains a Fuzzy Rule-based Ensemble, a tool for time series forecasting based on an ensemble of forecasts from several individual methods that is driven by a linguistic rule base created automatically from a large set of training time series.

Fuzzy rule base ensemble generated from data by linguistic associations mining

As there are many various methods for time series prediction developed but none of them generally outperforms all the others, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques, that combine several individual forecasts, are being proposed. In this contribution, we employ the so-called Fuzzy Rule-Based Ensemble. This method is constructed as a linear combination of a small number of forecasting methods where the weights of the combination are determined by fuzzy rule bases based on time series features such as trend, seasonality, or stationarity. For identification of fuzzy rule bases, we use the linguistic association mining. A huge experimental justification is provided.

Parallel mining of fuzzy association rules on dense data sets

The aim of this paper is to present a scalable parallel algorithm for fuzzy association rules mining that is suitable for dense data sets. Unlike most of other approaches, we have based the algorithm on the Webbs OPUS search algorithm [1]. Having adopted the master/slave architecture, we propose a simple recursion threshold technique to allow load-balancing for high scalability.

Excluding features in fuzzy relational compositions

Incorporation of the excluding features in fuzzy relational systems is proposed.Three alternatives ways of the incorporation are introduced.Their coincidence is investigated.Theoretical properties are studied.Application potential and impact is demonstrated on a real biological problem (Odonata identification). The aim of this paper is, first, to recall fuzzy relational compositions (products) and, to introduce an idea, how excluding features could be incorporated into the theoretical background. Apart from definitions, we provide readers with a theoretical investigation. This investigation addresses two natural questions. Firstly, under which conditions (in which underlying algebraic structures) the given three natural approaches to the incorporation of excluding symptoms coincide. And secondly, under which conditions, the proposed incorporation of excluding features preserves the same natural and desirable properties similar to those preserved by fuzzy relational compositions.The positive impact of the incorporation on reducing the suspicions provided by the basic circlet composition without losing the possibly correct suspicion is demonstrated on a real taxonomic identification (classification) of Odonata. Here, we demonstrate how the proposed concept may eliminate the weaknesses provided by the classical fuzzy relational compositions and, at the same time, compete with powerful machine learning methods. The aim of the demonstration is not to show that proposed concept outperforms classical approaches, but to show, that its potential is strong enough in order to complete them or in order to be combined with them and to use its different nature.

Interest measures for fuzzy association rules based on expectations of independence

Lift, leverage, and conviction are three of the best commonly known interest measures for crisp association rules. All of them are based on a comparison of observed support and the support that is expected if the antecedent and consequent part of the rule were stochastically independent. The aim of this paper is to provide a correct definition of lift, leverage, and conviction measures for fuzzy association rules and to study some of their interesting mathematical properties.

Fast evaluation of t-norms for fuzzy association rules mining

The aim of this paper is to present a bitwise approach on evaluation of fuzzy t-norms. T-norms are functions that generalize the notion of conjunction, and as such play an important role in fuzzy association rule mining process. Efficient algorithms for batch evaluation of the most common t-norms is proposed that minimizes computation time as well as memory space requirements at the cost of user-adjustable loss of precision of the membership degrees.

Fuzzy Rule-Based Ensemble for Time Series Prediction: Progresses with Associations Mining

As there are many various methods for time series prediction developed but none of them generally outperforms all the others, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques, that combine more individual forecasts, are being proposed. In this contribution, we employ the so called fuzzy rule-based ensemble. This method is constructed as a linear combination of a small number of forecasting methods where the weights of the combination are determined by fuzzy rule bases based on time series features such as trend, seasonality, or stationarity. For identification of fuzzy rule base, we use linguistic association mining. An exhaustive experimental justification is provided.

Mining linguistic associations for emergent flood prediction adjustment

Floods belong to the most hazardous natural disasters and their disaster management heavily relies on precise forecasts. These forecasts are provided by physical models based on differential equations. However, these models do depend on unreliable inputs such as measurements or parameter estimations which causes undesirable inaccuracies. Thus, an appropriate data-mining analysis of the physical model and its precision based on features that determine distinct situations seems to be helpful in adjusting the physical model. An application of fuzzy GUHA method in flood peak prediction is presented. Measured water flow rate data from a system for flood predictions were used in order to mine fuzzy association rules expressed in natural language. The provided data was firstly extended by a generation of artificial variables (features). The resulting variables were later on translated into fuzzy GUHA tables with help of Evaluative Linguistic Expressions in order to mine associations. The found associations were interpreted as fuzzy IF-THEN rules and used jointly with the Perception-based Logical Deduction inference method to predict expected time shift of flow rate peaks forecasted by the given physical model. Results obtained from this adjusted model were statistically evaluated and the improvement in the forecasting accuracy was confirmed.

Undefined Values in Fuzzy Logic

The main objective of this paper is to propose an extended algebra of truth values by special truth values which may have several interpretations, such as “undefined”, “non-applicative” “overdetermined”, “undetermined”, etc. In this paper, we will analyze several situations, where the non-existent data may come from, and show within a fuzzy sets framework that different cases of non-existence have to be carefully treated and interpreted in a different way.