Decision Table Research Articles

A Method to Handle the Missing Values in Multi-Criteria Sorting Problems Based on Dominance Rough Sets

The handling of missing attribute values remains a challenging and problematic issue in data analysis. Imputation techniques are key procedures used to deal with missing attribute values. However, although these methods are widely used, they cause data bias. Rough set theory, a unique mathematical tool for decision making under uncertainty, overcomes this problem by properly adjusting the relationships. Rough sets are often preferred in both classification and sorting problems. The aim of sorting problems is to sort the objects in the decision table (DT) from best to worst and/or to select the best one. For this purpose, it is necessary to obtain a pairwise comparison table (PCT) from the DT. However, in the presence of missing values, the transformation from DT to PCT is not feasible because there are no ranking methods in the literature for sorting problems based on rough sets. To address this limitation, this paper presents a way to transform from DT to PCT and introduces a generalization of the relation belonging to the “do not care” type of missing values in the dominance-based rough set approach (DRSA) to the decision support tool jRank. We also adapted the DomLem algorithm to enable it to work in PCT with missing values. We applied our method step by step to a decision table with 11 objects and investigated the effect of missing values. The experimental results showed that our proposed approach captures the semantics of `do not care’ type missing values.

The performance of priority rules for the decentralized resource-constrained multi-project scheduling

Decentralized resource-constrained multi-project scheduling (DRCMPSP) is becoming increasingly common in construction, supply chains, and many other industrial disciplines. DRCMPSP faces difficult decisions in resolving resource conflicts to generate a baseline schedule to optimize global objectives. We propose an agent-based approach to address the DRCMPSP based on two global objectives: average project delay and total project delay. A heuristic based on the priority rule (PR) is developed to coordinate the global resource allocation. A comprehensive analysis of 30 PRs was conducted on 16,000 portfolios containing 48,000 projects . We confirmed that using the same PR to allocate global resources on all occasions often results in unnecessarily poor performance. The best PR depends on project and portfolio characteristics such as serial/parallel indicators, global resource distribution, and tightness. Moreover, the best PR differs from various perspectives (e.g., projects and portfolios). We summarized our results in three decision tables and further distilled these results for practical use, which only provide a rough estimate of the project and portfolio characteristics.

Landslide risk assessment combining kernel extreme learning machine and information value modeling-A case study of Jiaxian Country of loess plateau, China

Landslide risk mapping can be an effective reference for disaster mitigation and land use planning, but the modelling process involves multidisciplinary knowledge which leads to its complexity. In this study, Jiaxian County in Shaanxi Province on the Loess Plateau of China, served as the study area, primarily characterized by Quaternary loess-covered geomorphology, with an average rainfall of about 400 mm annually. Soil erosion and human engineering activities have contributed to significant slope failures, posing threats to local residents and infrastructure. A reasonable inventory of landslides in the region was established by field survey combined with aerial imagery, allowing for characterization of their development and spatial distribution. Nine thematic maps related to landslide occurring and three vulnerability maps were prepared as influencing factors for landslide risk assessment. Subsequently, landslide susceptibility and hazard were evaluated using a kernel extreme learning machine (KELM) and information value (IV) model, followed by map validation. A decision table was then employed to generate the landslide risk map. The results of landslide hazard mapping showed that the historical landslide events were mainly developed in the central part of the study area, particularly concentrated near the developed river network. Integration of overall risk elements suggested that landslide risks in the study area were generally at a low level. Besides, a total of 0.25 % and 2.05 % of the areas were classified as having very high and high landslide risk levels, respectively, where 65.11 % of inventory landslides occurred. Therefore, the proposed procedure is a valuable tool for assessing landslide risk in Jiaxian Country.

Majority of Ruptures in Large Continental Strike-Slip Earthquakes Are Unilateral: Permissive Evidence for Hybrid Brittle-to-Dynamic Ruptures

Abstract Finite-element models of neotectonics require transform faults to rupture seismically even where preseismic shear stresses are low, presumably by dynamic-weakening mechanisms. A long-standing objection is that, if a rupture initiated at an asperity with high static friction stresses, which then transitioned to low dynamic-weakening stresses, local stress drop would be near total and on the order of 80 MPa, which is 4×–40× greater than observed. But the 5 Mw ≥ 7.8 transform earthquakes since 2000 initially ruptured on the branch faults of small net slip (Stein and Bird, 2024). If the slip initiates on a branch fault with different slip physics and no dynamic weakening, this solves the stress-drop problem. We propose that most large shallow earthquakes are hybrid ruptures, which begin on branch faults of small slip with high shear stresses, and then continue propagating on a connected dynamically weakened fault of large slip, even where shear stresses are low. One prediction of this model is that most large shallow ruptures should be unilateral. We test this prediction against the 100 largest (m ≥ 6.49) shallow continental strike-slip earthquakes 1977–2022, using information from the Global Centroid Moment Tensor and International Seismological Centre catalogs. The differences in time and location between the epicenter and the epicentroid define a horizontal “migration” velocity vector for the evolving centroid of each rupture. Early aftershock locations are summarized by a five-parameter elliptical model. Using the geometric relations between these (and mapped traces of active faults) and guided by a symmetrical decision table, we classified 55 ruptures as apparently unilateral, 30 as bilateral, and 15 as ambiguous. Our finding that a majority (55%–70%) of these ruptures are unilateral permits the interpretation that a majority of ruptures are hybrids, both in terms of geometry (branch fault to transform) and in terms of the physics of their fault slip.

Peri-ictal psychiatric manifestations in people with epilepsy: An umbrella review.

We aimed to conduct an umbrella review to summarize the existing evidence regarding the prevalence of peri-ictal psychiatric manifestations (PM) in people with epilepsy (PWE) including pre-ictal, ictal, and postictal stages. Databases were searched up to June 2023 for systematic reviews (SR) of observational studies that included patients with epilepsy peri-ictal PM. Data selection, data extraction, and risk of bias assessment (with the AMSTAR-2 instrument) were performed by two independent reviewers. We performed a narrative synthesis using previous guidelines. We used a self-developed decision table according to the GRADE system adapted for narrative outcomes if the certainty of outcomes was not determined by systematic review authors. Four SRs were included comprising 66 primary studies (n = 10 217). Three SRs evaluated one period (pre-ictal, ictal, and postictal), and one did not determine it. During the pre-ictal period, the more prevalent symptom was confusion, although with a low certainty (due to the heterogeneity and serious risk of bias). One systematic review that only included case reports evaluated the ictal period, finding mood/anxiety disorders, psychosis, and personality changes. The postictal period included the most PM (anxiety: 45.0% and depressive symptoms: 43.0%), with very low certainty, due to risk of bias, potential publication bias, heterogeneity, and failure to report the confidence intervals. With very low certainty, epileptic periods are characterized by a wide spectrum of PM, being postictal symptoms the most prevalent, predominantly anxiety, and depressive symptoms. Further understanding of these PM of epilepsy could improve the attention of the people with epilepsy. In this review of reviews, we summarize the frequency in which psychiatric manifestations occur in relation to an epileptic seizure. A total of 10 217 patients were reported in the reviews. The most common manifestations included symptoms of anxiety and depression, as well as changes in the normal behavior of the patient. These manifestations occurred most frequently right after the seizure finished.

Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method.

A new approach for attribute reduction from decision table based on intuitionistic fuzzy topology

A new approach for attribute reduction from decision table based on intuitionistic fuzzy topology

DECISION-MAKING SUPPORT TECHNOLOGIES IN THE FORMATION OF QUALITY MANAGEMENT SYSTEMS AT VEHICLE REPAIR ENTERPRISES

In recent years, the strategy of creating a safely functioning and efficient transport complex of Ukraine integrated into the world transport network has been fixed. Therefore, the creation of a progressive system of product quality management in the conditions of car repair enterprises is considered an urgent scientific and technical task. The quality management system is a management subsystem of an industrial enterprise. Modern production systems are based on TQM principles and widely use ISO 9001 certification. At the same time, certification is based on independent third-party audits (a conformity assessment organization). The wide spread of the standard is based on the fact that it contains generalized requirements (principles) for the organization of product quality control, which can be used at enterprises that differ significantly both in terms of specialization and the scale of production (depots or car repair plants). But this is the «bottleneck» of the standard – the lack of consideration of specialized production conditions makes it impossible to compensate for critical factors affecting the final quality of products. Currently, the accepted approaches to quality assessment do not fully take into account the factors that affect the final quality of products. In order to put all market participants - railway operators, manufacturers, rolling stock repairers and suppliers in mutually beneficial conditions, a single International Railway Industry Standard ISO / TS 22163 International Railway Industry Standart (IRIS) was developed.For the effective use of tools for ensuring the quality of rail car repair, it is considered necessary to use modern management decision support systems. They will allow real-time monitoring of production processes. It is possible to use the method of fuzzy sets to improve the quality management systems of wagon repairs based on the development of separate control algorithms with decision tables. In order to meet the requirements of international quality standards, it is necessary to significantly modernize management approaches based on the creation of progressive decision support systems.

Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes.

In this paper, we consider classes of decision tables with many-valued decisions closed under operations of the removal of columns, the changing of decisions, the permutation of columns, and the duplication of columns. We study relationships among three parameters of these tables: the complexity of a decision table (if we consider the depth of the decision trees, then the complexity of a decision table is the number of columns in it), the minimum complexity of a deterministic decision tree, and the minimum complexity of a nondeterministic decision tree. We consider the rough classification of functions characterizing relationships and enumerate all possible seven types of relationships.

Concept cognition for knowledge graphs: Mining multi-granularity decision rule

As part of cognitive intelligence, concept cognition for knowledge graphs aims to clearly grasp the typical characteristics of the things referred to by the concept, which can provide prior knowledge for machine understanding and thinking. Different from concept learning and formal concept analysis that learn new concepts from data and the general decision rule that comes from an independent decision table, this paper cognizes an existing concept by decision rules that come from multiple granularities. Specifically, 1) concept cognition for knowledge graphs is realized from the perspective of mining multi-granularity decision rule. 2) Decision tables corresponding to four granularities form a multi-granularity decision table group, and then the result from coarser granularity can guide and help obtaining the result from finer granularity. 3) We propose a framework for mining multi-granularity decision rules, which involves going from a multi-granularity decision table group to the frequent maximal attribute patterns to the decision rules to the credible decision rules. Finally, we verified effectiveness of dividing positive and negative data, monotonicity of attribute patterns in a multi-granularity decision table group, and downward monotonicity of credibility, and observed the impact of the parameter min_cov and min_conf on execution times.

Recommendations to minimize tooth root remodeling in patients undergoing maxillary osteotomies

The purpose of this study was to report root remodeling/resorption percentages of maxillary teeth following the different maxillary osteotomies; i.e. one-piece, two-pieces, three-pieces Le Fort I, surgically assisted rapid palatal expansion (SARPE). The possibility of relationships between root remodeling and various patient- and/or treatment-related factors were further investigated. A total of 110 patients (1075 teeth) who underwent combined orthodontic and orthognathic surgery were studied retrospectively. The sample size was divided into: 30 patients in one-piece Le Fort I group, 30 patients in multi-pieces Le Fort I group, 20 patients in SARPE group and 30 patients in orthodontic group. Preoperative and 1 year postoperative cone beam computed tomography (CBCT) scans were obtained. A validated and automated method for evaluating root remodeling and resorption in three dimensions (3D) was applied. SARPE group showed the highest percentage of root remodeling. Spearman correlation coefficient revealed a positive relationship between maxillary advancement and root remodeling, with more advancement contributing to more root remodeling. On the other hand, the orthodontic group showed a negative correlation with age indicating increased root remodeling in younger patients. Based on the reported results of linear, volumetric and morphological changes of the root after 1 year, clinical recommendations were provided in the form of decision tree flowchart and tables. These recommendations can serve as a valuable resource for surgeons in estimating and managing root remodeling and resorption associated with different maxillary surgical techniques.

Feature selection based on contradictory state sequence for multi-scale interval valued decision table

In the context of selecting features for multi-scale interval valued decision table (MIVDT), conventional research approaches encounter difficulties including elevated data complexity, diminished computational efficiency, and limited model generalization capacity. To overcome these difficulties, feature selection methods based on contradictory state sequence (CSS) and fuzzy contradictory state sequence (FCSS) are proposed in this paper. Initially, MIVDT is established. According to the characteristics of interval value, a more thorough and impartial metric for assessing the inclusion degree is suggested, along with similarity relation based on the metric. Subsequently, the introduction of the first contradictory object allows for the delineation of contradictory state and fuzzy contradictory state, which serve to characterize the consistency of MIVDT. These two concepts are proposed to enhance the accuracy and efficiency of capturing key information in intricate data. Additionally, rapid feature selection algorithms are suggested, which rely on CSS and FCSS. In contrast to conventional feature selection approaches, the algorithms introduced in this study demonstrate superior computational efficiency and enhanced generalization capabilities when confronted with intricate datasets. In twelve open source datasets, the upper limit for the quantity of objects is 110341, and the average accuracy values of experiments under two parameter combinations are 99.54% and 97.08% respectively. The results of experiments demonstrate that the algorithms introduced are capable of efficiently identifying a novel feature subset from intricate datasets within a shorter timeframe.

ANALYSIS USING ANT COLONY OPTIMIZATION TECHNIQUES TO MINE LUNG CANCER DATA FOR THE PURPOSE OF INCREASING OR DECREASING THE DISEASE PREDICTION VALUE

The leading cause of death for both men and women is cancer. The unchecked proliferation of aberrant cells that begin in one or both lung most often in the cell lining the air passageways. Small cell lung cancer and non-small cell lung cancer are the two primary forms. Non smokers Get Lung Cancer at a Rate of 10–15%. Smokers Make Up 50% of the Case. The longer someone smokes and the more cigarettes they smoke, the higher their risk of developing lung cancer. Lung cancer has become more common. Age, sex, wheezing, shortness of breath, and chest pain are among the symptoms that can indicate a patient's likelihood of developing lung cancer. Data mining algorithms, such as classification, decision tables, naïve-based, ant colony optimization, lung cancer prediction, and data mining techniques, are used to detect lung cancer disease in its early stages. According to this paper, early detection of lung cancer can completely cure the disease and help doctors save patients' lives. Ant colony optimization data mining techniques are useful for improving or decreasing the disease prediction value of lung cancer data.

Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting doselocations.

A decision fusion-based method for global sensitivity analysis of complicated experiments

In real world, the relationship between experimental input and output has become more and more complex, which brings great challenges to the sensitivity analysis. This paper proposes a decision fusion based global sensitivity analysis method for complicated experiments, which not only provides quantitative evluation of the input factor influence on experimental results, but also mines the correlation and form the explicit criteria in IF-THEN fomation for further guidance. The theory of decision information system and continuous attribute discretization is presented first for transforming the experimental input and output into a decision table. In order to calculate the sensitivity of the factors and extract valid correlation criterions between conditional attributes and decision attributes simultaneously, the discrimination matrx is utilized for attribute reduction. Then a sensitivity analysis method based on decision fusion is proposed by organically assembling experiment design, attribute discretization, the discrimization matrix, and attribute reduction. Finally, the effectiveness and practicality of the proposed method were verified by the application of sensitivity analysis in hypersonic vehicle re-entry trajectory experiment.

Design methodology for the control logic of flexible production systems and related logistics

The ever-shorter product life cycle entails a shortening of the life cycle of the production site and production systems as well. One of the biggest advantages of flexible production systems is that new products can be introduced to them continuously, so that their life cycle can be significantly extended. Flexible manufacturing systems, on the other hand, also require flexible control, the logic of which must be defined already in the design phase. The main elements of the design methodology of the production and logistics layout that make up the research are the information relationship chart and the related decision tables, which are suitable for speeding up the design and later for facilitating the creation of a digital twin and control logic programming.

Smart housing adaptations register with user matching functionalities for different mobility categories for the social housing sector

PurposeThe lack of a proper register to store, match and display information on the adapted property has led to a waste of resources and prolonged delays in matching the disabled and elderly people with appropriate properties. This paper presents the development of a Housing Adaptations Register with user-matching functionalities for different mobility categories. The developed system accurately captures and documents adapted home information to facilitate the automated matching of disabled/aged applicants needing an adapted home with suitable property using banding, mobility and suitability index.Design/methodology/approachA theoretical review was conducted to identify parameters and develop adaptations register construct. A survey questionnaire approach to rate the 111 parameters in the register as either moderate, desirable or essential before system development and application. The system development relied on DSS modelling to support data-driven decision-making based on the decision table method to represent property information for implementing the decision process. The system is validated through a workshop, four brainstorming sessions and three focus group exercises.FindingsDevelopment of a choice-based system that enables the housing officers or the Housing Adaptations Register coordinators to know the level of adaptation to properties and match properties quickly with the applicants based on their mobility status. The merits of the automated system include the development of a register to capture in real-time adapted home information to facilitate the automated matching of disabled/aged applicants. A “choice-based” system that can map and suggest a property that can easily be adapted and upgraded from one mobility band to the other.Practical implicationsThe development of a housing adaptation register helps social housing landlords to have a real-time register to match, map and upgrade properties for the most vulnerable people in our society. It saves time and money for the housing associations and the local authorities through stable tenancy for adapted homes. Potentially, it will promote the independence of aged and disabled people and can reduce their dependence on social and healthcare services.Originality/valueThis system provides the local authorities with objective and practical tools that may be used to assess, score, prioritise and select qualified people for appropriate accommodation based on their needs and mobility status. It will provide a record of properties adapted with their features and ensure that matching and eligibility decisions are consistent and uniform.

Detecting Android Malware by Mining Enhanced System Call Graphs

The persistent threat of malicious applications targeting Android devices has been growing in numbers and severity. Numerous techniques have been utilized to defend against this thread, including heuristic-based ones, which are able to detect unknown malware. Among the many features that this technique uses are system calls. Researchers have used several representation methods to capture system calls, such as histograms. However, some information may be lost if the system calls as a feature is only represented as a 1-dimensional vector. Graphs can represent the interaction of different system calls in an unusual or suspicious way, which can indicate malicious behavior. This study uses machine learning algorithms to recognize malicious behavior represented in a graph. The system call graph was fed into machine learning algorithms such as AdaBoost, Decision Table, Naïve Bayes, Random Forest, IBk, J48, and Logistic regression. We further employ a series feature selection method to improve detection accuracy and eliminate computational complexity. Our experiment results show that the proposed method has reduced feature dimension to 91.95% and provides 95.32% detection accuracy.

Pheromone-guided parallel rough hypercuboid attribute reduction algorithm

In knowledge discovery and data mining, removing redundant and irrelevant data attributes is crucial. Traditional algorithms, however, struggle with efficiency in high-dimensional big data contexts. To solve this problem, this paper proposes a novel global search attribute reduction method namely RHC-IGWO (Rough Hypercuboid Structure via Improved Grey Wolf Optimizer) by integrating the rough hypercuboid method and Improved Grey Wolf Optimizer (IGWO) with the pheromone mechanism. The algorithm is embedded into the Apache Spark parallel computing framework (Parallel computing Rough Hypercuboid Structure via Improved Grey Wolf Optimizer, PcRHC-IGWO) to accelerate and simplify the attribute reduction process. The algorithm divides the decision table into several independent blocks, introduces the pheromone mechanism to simulate the wolf pack behavior, and uses the IGWO for global search, which is conducive to efficient local search and global information sharing between individuals. The position of the individual is initialized by calculating the relevance between the attributes, and the pheromone value is dynamically updated according to the reduction quality. This allows automatically giving more search focus to more promising attribute regions. Experiments with public and real datasets demonstrate the RHC-IGWO algorithm's significant speedup and its efficacy in maintaining or enhancing classification accuracy. Particularly noteworthy is its performance on schizophrenia datasets, where the proposed method achieves outstanding classification accuracies of 86.2%, 88.89%, and 92.86% across various classifiers. These results not only demonstrate its effectiveness but also underline its potential in advanced data analysis scenarios. Additionally, on some large-scale datasets, the time required for processing has been reduced by 85.71%, showcasing the algorithm's efficiency in handling complex and voluminous data.

Improving Concordance Between Clinicians With Australian Guidelines for Bowel Cancer Prevention Using a Digital Application: Randomized Controlled Crossover Study.

Australia's bowel cancer prevention guidelines, following a recent revision, are among the most complex in the world. Detailed decision tables outline screening or surveillance recommendations for 230 case scenarios alongside cessation recommendations for older patients. While these guidelines can help better allocate limited colonoscopy resources, their increasing complexity may limit their adoption and potential benefits. Therefore, tools to support clinicians in navigating these guidelines could be essential for national bowel cancer prevention efforts. Digital applications (DAs) represent a potentially inexpensive and scalable solution but are yet to be tested for this purpose. This study aims to assess whether a DA could increase clinician adherence to Australia's new colorectal cancer screening and surveillance guidelines and determine whether improved usability correlates with greater conformance to guidelines. As part of a randomized controlled crossover study, we created a clinical vignette quiz to evaluate the efficacy of a DA in comparison with the standard resource (SR) for making screening and surveillance decisions. Briefings were provided to study participants, which were tailored to their level of familiarity with the guidelines. We measured the adherence of clinicians according to their number of guideline-concordant responses to the scenarios in the quiz using either the DA or the SR. The maximum score was 18, with higher scores indicating improved adherence. We also tested the DA's usability using the System Usability Scale. Of 117 participants, 80 were included in the final analysis. Using the SR, the adherence of participants was rated a median (IQR) score of 10 (7.75-13) out of 18. The participants' adherence improved by 40% (relative risk 1.4, P<.001) when using the DA, reaching a median (IQR) score of 14 (12-17) out of 18. The DA was rated highly for usability with a median (IQR) score of 90 (72.5-95) and ranked in the 96th percentile of systems. There was a moderate correlation between the usability of the DA and better adherence (rs=0.4; P<.001). No differences between the adherence of specialists and nonspecialists were found, either with the SR (10 vs 9; P=.47) or with the DA (13 vs 15; P=.24). There was no significant association between participants who were less adherent with the DA (n=17) and their age (P=.06), experience with decision support tools (P=.51), or academic involvement with a university (P=.39). DAs can significantly improve the adoption of complex Australian bowel cancer prevention guidelines. As screening and surveillance guidelines become increasingly complex and personalized, these tools will be crucial to help clinicians accurately determine the most appropriate recommendations for their patients. Additional research to understand why some practitioners perform worse with DAs is required. Further improvements in application usability may optimize guideline concordance further.