Decision Rule Systems Research Articles

Development of mathematical methods for diagnosing kidney diseases using fuzzy set tools

An approach based on fuzzy set theory is presented in the scientific article to enhance the efficiency of diagnosing kidney diseases by decreasing the time required for making medical decisions. The suggested approach employs fuzzy models and algorithms that consider the uncertainty and variability of clinical data to optimize the assessment of the functional state of the kidneys, taking into account various risk factors and individual characteristics of patients. The paper suggests a technique to develop a system of fuzzy decision rules. This technique combines E. Shortliff’s iterative rules with functions from the studied classes of kidney diseases. Mathematical modeling and experimental studies have indicated relatively high effectiveness in classifying different forms of kidney diseases. The results can be used to formulate intelligent decision support systems in clinical practice and improve diagnostic and monitoring processes. Moreover, the findings may aid in shaping more targeted and effective health policies at the national and regional levels, enhancing access to healthcare, and promoting the population’s overall health.

Bounds on depth of decision trees derived from decision rule systems with discrete attributes

Bounds on depth of decision trees derived from decision rule systems with discrete attributes

Interpretation of a trained neural network based on genetic algorithms

Introduction: Artificial intelligence development strategy involves the use of deep machine learning algorithms in order to solve various problems. Neural network models trained on specific data sets are difficult to interpret, which is due to the “black box” approach when knowledge is formed as a set of interneuronal connection weights. Purpose: Development of a discrete knowledge model which explicitly represents information processing patterns encoded by connections between neurons. Methods: Adaptive quantization of a feature space using a genetic algorithm, and construction of a discrete model for a multidimensional OLAP cube with binary measures. Results: A genetic algorithm extracts a discrete knowledge carrier from a trained neural network. An individual's chromosome encodes a combination of values of all quantization levels for the measurable object properties. The head gene group defines the feature space structure, while the other genes are responsible for setting up the quantization of a multidimensional space, where each gene is responsible for one quantization threshold for a given variable. A discrete model of a multidimensional OLAP cube with binary measures explicitly represents the relationships between combinations of object feature values and classes. Practical relevance: For neural network prediction models based on a training sample, genetic algorithms make it possible to find the effective value of the feature space volume for the combinations of input feature values not represented in the training sample whose volume is usually limited. The proposed discrete model builds unique images of each class based on rectangular maps which use a mesh structure of gradations. The maps reflect the most significant integral indicators of classes that determine the location and size of a class in a multidimensional space. Based on a convolution of the constructed class images, a complete system of production decision rules is recorded for the preset feature gradations.

Data Capture and Multimodal Learning Analytics Focused on Engagement With a New Wearable IoT Approach

Increasing school dropout rates are a problem in many educational systems, with student disengagement being one significant factor. Learning analytics is a new field with a key role in educational institutions in the coming years. It may help make strategic decisions to reduce student disengagement. The use of technology in educational environments has grown significantly and, with it, awareness of the importance of student engagement. We exploit tracking and wearable technologies to increase user engagement in learning processes, exploring also the area of multimodal learning analytics (MMLA). We use wearables and Internet of Things for education, an interactive and collaborative system designed to improve motivation and learning. This article presents the results obtained in different experiments conducted in a secondary school in a long-term participatory learning context. The captured data were analyzed and used to identify different students’ behavior patterns, showing their progress and motivation. Subsequently, from the captured data and aiming at a decision-making phase, we used machine learning techniques and MMLA methodologies to construct models able to “explain” when student engagement is present, so this knowledge can later be exploited. In particular, we chose decision trees and rule systems based on a set of variables with proven relevance to the problem. The evaluation of this novel engagement classification system confirms the high performance of these variables. The rules obtained, which can be easily interpreted by a nonexpert, help the teacher to observe, analyze, and make decisions with the purpose of fostering engagement.

The Concept of Fuzzy Linguistic Labels for Studying Decision Systems in Air Transportation

This paper presents two approaches based on the concept of fuzzy linguistic label, which can be applied in the domain of air transportation, especially in situations where human factor, uncertainty, and subjectivity play an important role. Firstly, we consider the problem of constructing a decision model of a pilot who performs a task of aircraft control. In order to describe the pilot-aircraft interactions, an information system is determined by selecting the perception and action attributes used during the control of aircraft. Labeled fuzzy rough sets-based approach is used for analysis of a decision table with fuzzy attributes. We get a system of fuzzy decision rules which represents the pilot’s control actions. Secondly, we demonstrate a multi-criteria decision-making procedure by applying fuzzy linguistic labels to express the subjective preferences of an expert in the task of selecting a suitable aircraft from a set of alternatives. Labeled fuzzy rough sets are used for analyzing the decision system of the expert. The linguistic labels obtained from the expert are applied for calculating the final ranking, according to preferences of the decision-maker with respect to fuzzy attributes and their linguistic values.

Bi-criteria optimization problems for decision rules

We consider bi-criteria optimization problems for decision rules and rule systems relative to length and coverage. We study decision tables with many-valued decisions in which each row is associated with a set of decisions as well as single-valued decisions where each row has a single decision. Short rules are more understandable; rules covering more rows are more general. Both of these problems—minimization of length and maximization of coverage of rules are NP-hard. We create dynamic programming algorithms which can find the minimum length and the maximum coverage of rules, and can construct the set of Pareto optimal points for the corresponding bi-criteria optimization problem. This approach is applicable for medium-sized decision tables. However, the considered approach allows us to evaluate the quality of various heuristics for decision rule construction which are applicable for relatively big datasets. We can evaluate these heuristics from the point of view of (i) single-criterion—we can compare the length or coverage of rules constructed by heuristics; and (ii) bi-criteria—we can measure the distance of a point (length, coverage) corresponding to a heuristic from the set of Pareto optimal points. The presented results show that the best heuristics from the point of view of bi-criteria optimization are not always the best ones from the point of view of single-criterion optimization.

Bi-criteria optimization of decision trees with applications to data analysis

This paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms.

Intraoperative Clinical Decision Support for Anesthesia: A Narrative Review of Available Systems.

With increasing adoption of anesthesia information management systems (AIMS), there is growing interest in utilizing AIMS data for intraoperative clinical decision support (CDS). CDS for anesthesia has the potential for improving quality of care, patient safety, billing, and compliance. Intraoperative CDS can range from passive and post hoc systems to active real-time systems that can detect ongoing clinical issues and deviations from best practice care. Real-time CDS holds the most promise because real-time alerts and guidance can drive provider behavior toward evidence-based standardized care during the ongoing case. In this review, we describe the different types of intraoperative CDS systems with specific emphasis on real-time systems. The technical considerations in developing and implementing real-time CDS are systematically covered. This includes the functional modules of a CDS system, development and execution of decision rules, and modalities to alert anesthesia providers concerning clinical issues. We also describe the regulatory aspects that affect development, implementation, and use of intraoperative CDS. Methods and measures to assess the effectiveness of intraoperative CDS are discussed. Last, we outline areas of future development of intraoperative CDS, particularly the possibility of providing predictive and prescriptive decision support.

How Best to Obtain Valid, Verifiable Data Online From Male Couples? Lessons Learned From an eHealth HIV Prevention Intervention for HIV-Negative Male Couples.

BackgroundAs interest increases in the development of eHealth human immunodeficiency virus (HIV)-preventive interventions for gay male couples, Web-based methods must also be developed to help increase the likelihood that couples enrolled and data collected from them represent true unique dyads. Methods to recruit and collect reliable and valid data from both members of a couple are lacking, yet are crucial for uptake of novel sexual health and HIV-prevention eHealth interventions. Methods to describe best practices to recruit male couples using targeted advertisements on Facebook are also lacking in the literature, yet could also help in this uptake.ObjectiveThe objective of our study was to describe challenges and lessons learned from experiences from two phases (developmental phase and online randomized controlled trial [RCT]) of an eHealth HIV-prevention intervention for concordant HIV-negative male couples in terms of (1) recruiting male couples using targeted advertisements on Facebook, (2) validating that data came from two partners of the couple, and (3) verifying that the two partners of the couple are in a relationship with each other.MethodsThe developmental phase refined the intervention via in-person focus groups, whereas the pilot-testing phase included an online RCT. For both phases, couples were recruited via targeted Facebook advertisements. Advertisements directed men to a study webpage and screener; once eligible, participants provided consent electronically. A partner referral system was embedded in the consenting process to recruit the relationship partner of the participant. Both men of the couple had to meet all eligibility criteria—individually and as a couple—before they could enroll in the study. Verification of couples’ relationships was assessed via the concurrence of predetermined screener items from both partners, done manually in the developmental phase and electronically in the pilot-testing phase. A system of decision rules was developed to assess the validity that data came from two unique partners of a couple.ResultsSeveral important lessons were learned from these experiences, resulting in recommendations for future eHealth studies involving male couples. Use of certain “interests” and types of images (eg, shirtless) in targeted Facebook advertisements should be avoided or used sparingly because these interests and types of images may generate adverse reactions from a broader audience. Development of a systematic approach with predetermined criteria and parameters to verify male couples’ relationships is strongly recommended. Further, researchers are encouraged to develop a system of decision rules to detect and handle suspicious data (eg, suspicious email addresses/names, multiple entries, same IP address used in multiple entries) to help validate the legitimacy of male couples’ relationships online.ConclusionsThese lessons learned combined with recommendations for future studies aim to help enhance recruitment efforts and the validity and reliability of collecting dyadic data from male couples for novel eHealth HIV-preventive interventions.

Adaptive Neuro-Fuzzy Recognition Technology Intersecting Objects

The study discusses adaptive neuro-fuzzy methods of recognition of the multidimensional overlapping objects on the basis of the introduced concepts, generalized and modified operations of fuzzy set theory and neural networks. To improve recognition accuracy, proposed a combined approach including neural network analysis of generalized images based on Kohonen maps, and building systems fuzzy inference based on the identification of allocated clusters integral characteristics of the images. Using the derived system of diagnostic decision rules "If ... then" the comprehensive forecast map of oil South of Cheremshan field was built.

A hybrid approach to bilingual text-to-phoneme mapping

In this paper, we address the problem of bilingual text-to-phoneme (TTP) mapping in which the phonetic transcription of isolated written words must be found. In general, in the bilingual/multilingual TTP mapping for isolated words, two processing steps are applied to each input word. The language of each word is first identified and then the letters of the word are translated into their phonetic transcriptions according to the recognized language. We use the multilayer perceptron (MLP) neural network for the letter to phoneme conversion task and a hybrid approach composed of a MLP and a decision rule system for the language recognition task. We introduce a new bilingual TTP mapping system and we provide an analysis of the influence of several different factors on its phoneme accuracy. .

The Soil Management Assessment Framework

Erosion rates and annual soil loss tolerance (T) values in evaluations of soil management practices have served as focal points for soil quality (SQ) research and assessment programs for decades. Our objective is to enhance and extend current soil assessment efforts by presenting a framework for assessing the impact of soil management practices on soil function. The tool consists of three steps: indicator selection, indicator interpretation, and integration into an index. The tool's framework design allows researchers to continually update and refine the interpretations for many soils, climates, and land use practices. The tool was demonstrated using data from case studies in Georgia, Iowa, California, and the Pacific Northwest (WA, ID, OR). Using an expert system of decision rules as an indicator selection step successfully identified indicators for the minimum data set (MDS) in the case study data sets. In the indicator interpretation step, observed indicator data were transformed into unitless scores based on site‐specific algorithmic relationships to soil function. The scored data resulted in scientifically defensible and statistically different treatment means in the four case studies. The efficacy of the indicator interpretation step was evaluated with stepwise regressions using scored and observed indicators as independent variables and endpoint data as iterative dependent variables. Scored indicators usually had coefficients of determination (R2) that were similar or greater than those of the observed indicator values. In some cases, the R2 values for indicators and endpoint regressions were higher when examined for individual treatments rather than the entire data set. This study demonstrates significant progress toward development of a SQ assessment framework for adaptive soil resource management or monitoring that is transferable to a variety of climates, soil types, and soil management systems.

System-Wide Probabilities for Systems with Runs and Scans Rules

Systems that include multiple decision rules are encountered in many fields. We focus on systems with several states or levels where the rules determining when and how to move between the levels are based on runs or scans. Our target is to evaluate the proportion of time spent at each level. This information is valuable since it is required for constructing various cost functions. In this paper we address two questions: How to incorporate the system of levels and decision rules into a probabilistic setting and which measures to use in order to evaluate the proportion of time spent at each level. We take a waiting-time approach, and use special features of runs and scans distributions in order to investigate long and short-term measures of the time spent at the different levels of a system. We introduce a short term per-cycle measure and compare it with the finite-time measure. We find that the two measures can differ significantly.

Vector rational error correction

Abstract Systems of forward-looking linear decision rules can be formulated as vector ‘rational’ error correction models. The closed-form solution of the restricted error corrections is derived, and a full-information estimator is suggested. The error correction format indicates that the assumptions of convex adjustment costs and rational expectations impose different types of a priori restrictions on the dynamic structure of the error corrections. An empirical model of the producer decision rule for capital investment illustrates that the data rejects dynamic restrictions imposed by a standard model of adjustment costs but supports a more general description of convex frictions.

SOME BOUNDS ON MINIMAL DECISION TREE DEPTH

We investigate decision trees for decision tables. We present upper and lower bounds on the minimal decision tree depth. Some bounds are expressed by parameters of decision rule systems constructed for decision tables.

Modelling the Response of Vegetation in North-East China Transect to Global Change

Biogeographical models relating large-scale patterns of climate and vegetation provide a way of simulating vegetation response to global change. While scaling down these models to regional or landscape scale, climatic factors are not the only components that affect vegetation patterns; other environmental components, such as topography and parent materials, also play important roles in vegetation classification. Elevation and quaternary geology are applied as indices of topography and parent materials, along with climatic index of moisture (MI), to classify vegetation and then predict the response of vegetation in North-East China INTRODUCTION There are two approaches for development of models of global vegetation: bottom-up and the top-down. The topdown approach utilizes current equilibrium models of global vegetation patterns by relating large-scale patterns of climate and vegetation (Smith, Leemans & Shugart, 1994). This top-down approach modifies global vegetation models by defining functional types of vegetation. While scaling down global or continental models (e.g. Holdridge Life Zone Classification, Holdridge, 1967) to regional or landscape level, vegetation is affected not only by large-scale climatic factors but also by other regional environmental components, such as topography and soil parent materials. Topography alters local water balance and parent materials dominates local nutrient cycling. They are therefore useful indicators of vegetation classification in predicting vegetation response to global change. North-East China Transect (NECT) lies between 42-46? latitude and 106-134? longitude, covering an area of 700,000 km2. It has a sharp and very sensitive environmental gradient from coastal east to interior west; for example, transition of climate (primarily precipitation ranging from more than 1300 mm to less than 150 mm), soil (organic matter, C/N, efficient water, various ionic ? 1995 Blackwell Science Ltd. Transect to global change. The model is based on a vegetation classification decision rule system established by statistical analysis of environment: vegetation relationships. The classification has been validated and gives general vegetation response patterns and sensitivities to global change.

Can medical decisions be standardized? Should they be?

After a variety of regulatory and payment schemes have failed to stem the rising tide of health care cost, the Omnibus Budget Reconciliation Act of 1989 mandates the creation of a system of decision rules called practice parameters for appropriate medical action in many circumstances. A large body of practice guidelines already exists, but lacks the internal coherence required of a policy tool. Professional organizations therefore have developed attributes to achieve uniform style. However, little has been said or published about the constraints that might be imposed on the structure and content of an efficient and coherent system. The arguments presented here lead to the following conclusions: (a) process control is an inefficient tool to manage outcome--standards should reflect product control; (b) guidelines that proscribe are more likely to be supported by scientific data and consensus than those that prescribe; (c) the decision thresholds contained in such directives are policy choices rather than scientific imperatives; (d) neither decision analysis nor artificial intelligence is likely to readily influence medical decisions; and (e) as suggested by operations research, the development of practice parameters should concentrate on issues of therapeutic management in preference to issues of diagnostic activity.

Introduction to Decision Rule Systems

Decision rule systems may be used to assist special educators who must make timely, numerous, and complicated decisions. This paper defines decision rule systems and discusses their critical features and the advantages and disadvantages of their use in order to assist special educators in understanding decision rule systems. Further, the paper examines practical considerations and steps in selecting decision rule systems for use in classroom decision making.

A new imaging method and its application in gynecological treatment planning

Intracavitary afterloading therapy -- a customary system to treat malignant tumours in gynaecology -- has received an appropriate basis by development of intrauterine ultrasound, because on the one hand sonographic probe and afterloading probe produce the same geometric circumstances and on the other intrauterine ultrasound has proved to be a method for showing normal myometrium and detecting its pathologic modified regions, especially for localisation and infiltration depth of malignant processes. After computer assisted segmentation and reconstruction of organ slices in three dimensional domain various dose distributions are calculated in case of carcinoma of the body of the uterus. They are produced by various probes and times, during which the radioactive source stops. Using a system of decision rules, a therapy plan is selected, which is an optimum adaptation to the individual geometric circumstances.

An Economic Replacement Model

The report is based on a study of the replacement of Army vehicles. These vehicles are currently phased out of service by a system of decision rules called repair limits. Existing repair limit systems, although superior to group replacement policies, appear to be sub-optimum.