Design Of Decision Support System Research Articles

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis.

ObjectivesElectronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.Material and MethodsWe conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.ResultsThe review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.ConclusionWe summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.RegistrationPROSPERO (CRD42020203716)

Sistem Pendukung Keputusan Untuk Menentukan Customer Segment Pasar Salah Satu UMKM Di Bandung ( Foodendez ) Dengan Metode Decision Tree

Abstract Foodendez is a UMKM in Bandung which is well known for its products, namely Uthi & Mies Krispi Patin Fish Skin which is a best seller product at Foodendez. This business has been running for about 5 years.In these 5 years Foodendez already has its own customer / market segment for the sale of this Foodendez product, however, this Foodendez has a problem predicting the customer / market segment for product sales from Foodendez, this is not suitable. expected expectations, because in analyzing / predicting customers / market segments still using a simple method or it can only be said to only use initial predictions by guessing roughly what estimates are known from the business plan or by other methods that make this Foodendez product get the wrong customer / segment that market It is expected that before hand or can be said to change and have an effect which makes Foodendez's turn over change insignificantly, and until recently Foodendez has not had a customer / market segment that is truly significant or as expected, for example is the sale of Krispi Patin Fish Skin that is predicted by the customer. the segment is more people > 18 years old but it turns out that < 18 years old people also like Krispi Patin Fish Skin products and offline and online sales where it is predicted that online sales will get a significant gain, which turns out to be more income in offline sales. So the authors provide a solution with the design / scheme of Decision Support Systems that will determine the customer segment and market segment which will later find out which market segment and customer segment are more significant and know which targets are suitable for products from Foodendez so that Foodendez UMKM can know what steps must be taken to set clear and significant targets by looking at the results of the percentage of enthusiasts and the turnover given by the Decision Tree method. The method of activities carried out is taken by collecting data with the interview method. The result of this activity is a Decision Support System design / scheme where the design / scheme is the result of determining a more detailed and significant customer segment / market segment and according to what the owner of Foodendez UMKM wants. Keywords: Decision Tree , Customer Segment , Market Segment , Foodendez , Uthi & Mies

The conceptual design of intelligent spatial decision support system for the fishery-industry logistic

This paper describes the conceptual framework of ISDSS (Intelligence Spatial Decision Support System) for the fishery industry logistic. The decision problem involved is complex due to the fish’s seasonal, perishable, size variability characteristics as raw material. The ISDSS will be supported by three main models, i.e. raw material origin selection, distribution route selection and product tracking and tracing. Multiple criteria decision models will be used for raw material selection. Multi-objective optimization will be used for optimum route selection, considering time, distance, fish quality, and spatial information. Internet of Things (IoT) model will be used for product tracking and tracing system. The geographical information system (GIS) integration with the DSS will also be supported by Artificial Intelligence Technique, i.e. data mining in classifying and clustering the raw material characteristic and origin data. The data collection will be supported by the Internet of Things (IoT). The best technique for each model will be investigated further based on the field experiment and simulation.

Design of Digital and Intelligent Financial Decision Support System Based on Artificial Intelligence.

The quality of financial decision-making is very important to the future development of an enterprise, but it is often affected by the completeness of useful information for decision-making and the subjective factors of decision makers, and is often unstable. With the development of computer technology, the financial decision support system came into being, which improved the quality of financial decision to some extent. However, although the existing financial decision support system has achieved dataization to a certain extent, it still faces problems such as artificial leadership, insufficient intelligence, and poor decision-making efficiency, and cannot fully meet the needs of decision-makers. The explosion of artificial intelligence technology in recent years has provided potential improvements to financial decision support systems. In this article, we conduct a detailed analysis of the deficiencies in the current financial decision support system, build the mechanism and implementation path of the financial decision support system under artificial intelligence, and design a digital and intelligent financial decision support system. At the same time, we apply the proposed financial decision support system to the financial practice of X enterprise. Through the questionnaire survey, it is found that through the comprehensive application of artificial intelligence technology, the new system has a higher degree of intelligence than the existing system, and its construction can effectively improve the timeliness and accuracy of financial decision-making, while reducing the cost of financial decision-making. It is conducive to promoting the integration of management accounting and financial accounting.

Decision support for corporate sustainability: systems and stakeholders

ABSTRACT Decision support systems for corporate environmental management and ecological sustainability are proliferating, this paper explores their directions in a multifaceted literature via an integrative literature review. Besides delineating the interrelated areas of decision support systems for sustainability, we study the role of internal stakeholders in their implementation. In our review, we examine a relatively mature field (Environmental Decision Support System, EDSS) and an emerging field (Green Information System, Green IS), and their potential interplays. The importance of stakeholder inclusion in general, and the participation of end-users in the design and implementation of decision support systems, in particular, are analysed in the EDSS literature. The Green IS has a prospective future in decision support with its capacity to capture corporate environmental performance. Green IS literature, however, has just recently identified the significance of employee participation: barriers and potentials to engage employees have already been identified but still to be explored.

Data-driven decision making: new opportunities for DSS in data stream contexts

ABSTRACT Traditionally, Decision Support Systems (DSS) data were stored statically and persistently in a database. Increasing volume and intensity of information and data streams create new opportunities and challenges for DSS experts, data scientists, and decision makers. Novel data stream contexts require that we move beyond static DSS modelling techniques to support data-driven decision-making. Implementing incremental and/or adaptive algorithms may help to solve some of the challenges arising from data streams. This research investigates the use of these algorithms to better understand how their performance compares with more traditional approaches. We show that an adaptive DSS engine has the potential to identify errors and improve the accuracy of the model. We briefly identify how this approach could be applied to unexpected highly uncertain decision scenarios. Future research considers new opportunities to pursue a multidisciplinary approach to adaptive DSS design, development, and implementation leveraging emerging machine learning techniques in tackling complex decision problems.

Optimizing unbalanced freight deliveries in transportation networks

This paper reports a comparative analysis of the known methods for reducing open transportation problems to a balanced form in order to further optimize freight traffic based on them. A series of significant shortcomings have been revealed that largely narrow the scope of their application. A new method has been proposed, termed the method of proportional redistribution of cargo transportation volumes among participants in the transportation process, devoid of the identified shortcomings. The transportation problem is a special case of the general linear programming problem, to which one of the methods for solving it, namely the simplex one, can be applied. A procedure to construct a simplex table based on the data from the transport table has been described, as well as the algorithm of subsequent simplex transformations. A transportation problem is often stated in the form of a map of the location of transport hubs of cargo dispatch and destination. A matrix-network model has been proposed, which makes it possible to reduce the network representation to a matrix form with the subsequent finding of the optimal plan for cargo transportation. In order to identify the priority of methods for reducing open transportation problems to a balanced form, 100 transportation problems that are unbalanced in terms of the volume of cargo transportation were solved. That was done with the help of a designed decision support system for the management of freight transport. As a criterion, the best freight transportation plan was chosen. As a result, the simplex method proved the best in 48 cases, the coefficient method ‒ in 27, the dummy node method ‒ in 16, and the difference method ‒ in 9 cases. The use of a decision support system for the management of freight transport has increased its efficiency by an average of 25 %

Smart Pre-Seeding Decision Support System for Agriculture

In recent years, the Internet of Things (IoT) brings a new dimension for establishing a precision network connectivity of sensors, especially in the agriculture and farming industry, medical, economic, and several sectors of modern society. Agriculture is an important area for the sustainability of mankind engulfing manufacturing, security, and resource management. Due to the exponential diminishing of the resources, innovative techniques to support the subsistence of agriculture and farming. IoT aims to extend the use of internet technology to a large number of distributed and connected devices by representing standard and interoperable communication protocols. This paper brings up a solution by IoT, presents the design and implementation of a smart pre-seeding decision support system for agricultural modernization. This project is accomplished by understanding the real-time circumstances in the agriculture field using wireless technology that highlighted the features including pH and temperature sensors, hardware, mobile application, system’s frontend, and backend analysis, and stores the extracted information in the cloud using IoT. The system is made up of frontend data acquisition, data transmission, data processing, and reception, and is also experimentally validated to find out all possible crops that can be cultivated in a specific land with the required amount of fertilizers as well as the overall crops distribution lists.

Crew Autonomy During Simulated Medical Event Management on Long Duration Space Exploration Missions.

Our primary aim was to investigate crew performance during medical emergencies with and without ground-support from a flight surgeon located at mission control. There are gaps in knowledge regarding the potential for unanticipated in-flight medical events to affect crew health and capacity, and potentially compromise mission success. Additionally, ground support may be impaired or periodically absent during long duration missions. We reviewed video recordings of 16 three-person flight crews each managing four unique medical events in a fully immersive spacecraft simulator. Crews were randomized to two conditions: with and without telemedical flight surgeon (FS) support. We assessed differences in technical performance, behavioral skills, and cognitive load between groups. Crews with FS support performed better clinically, were rated higher on technical skills, and completed more clinical tasks from the medical checklists than crews without FS support. Crews with FS support also had better behavioral/non-technical skills (information exchange) and reported significantly lower cognitive demand during the medical event scenarios on the NASA-TLX scale, particularly in mental demand and temporal demand. There was no significant difference between groups in time to treat or in objective measures of cognitive demand derived from heart rate variability and electroencephalography. Medical checklists are necessary but not sufficient to support high levels of autonomous crew performance in the absence of real-time flight surgeon support. Potential applications of this research include developing ground-based and in-flight training countermeasures; informing policy regarding autonomous spaceflight, and design of autonomous clinical decision support systems.

Human factors applications in the design of decision support systems for population health: a scoping review

IntroductionPublic health professionals engage in complex cognitive tasks, often using evidence-based decision support tools to bolster their decision-making. Human factors methods take a user-centred approach to improve the design of...

Diagnosing, Managing, and Controlling COVID-19 using Clinical Decision Support Systems: A Study to Introduce CDSS Applications

Coronavirus disease (COVID-19) as an emerging disease decreases security among people from different countries. Sense of security can be raised via quick diagnosis of COVID-19, and its management and control using clinical decision support systems (CDSS) to prevent further spread of the disease. So, the aim of this study is to identify and introduce the applications of a CDSS in the diagnosis, management, and control of COVID-19. This cross-sectional study was conducted to identify and introduce the applications of CDSS in the diagnosis, management, and control of COVID-19. Based on the results of some meetings with infectious disease specialists and a general practitioner as well as reviewing the related literature, information about COVID-19 and CDSS was obtained. Then based on the information obtained, a questionnaire was designed electronically and distributed in a two-round Delphi method among 19 experts in the three fields of medical informatics, health information management, and infectious disease specialists. According to the literature and expert opinions, 35 applications of CDSS applications were identified in the four main groups of “diagnosis”, “medication”, “monitoring”, and “health services”. Eventually, a collective agreement was reached on 30 applications in the first and second rounds of Delphi. Among all the applications, the highest means were assigned to “monitoring the vital signs” and “helping diagnose infections and damaged lung tissue through CT scan”. Introducing these applications can provide general, basic knowledge of the design and implementation of clinical decision support systems in the real world to prevent irreversible complications and even many people’s death.

Pharmacogenomics: Relevance and opportunities for clinical pharmacology

Pharmacogenomics: Relevance and opportunities for clinical pharmacology

Design and Definition of a New Decision Support System Aimed to the Hierarchization of Patients Candidate to Be Admitted to Intensive Care Units

The triage processes prior to the assignation of healthcare resources in hospitals are some of the decision-making processes that more severely affect patients. This effect gets even worse in health emergency situations and intensive care units (ICUs). Aiming to facilitate the decision-making process, in this work the use of vague fuzzy numbers is proposed, aiming to define a multi-attribute patient hierarchization method to be used in emergency situations at hospital ICUs. The incorporation of fuzzy models allows for modelling the vagueness and uncertainty associated with decision criteria evaluation, with which more efficient support is provided to the decision-making process. After defining the methodology, the effectiveness of this new system for patient hierarchization is shown in a case study. As a consequence of that, it is proved that the integration of decision-support systems into healthcare environments results to be efficient and productive, suggesting that if a part of the decision process is supported by these systems, then the errors associated with wrong interpretations and/or diagnoses might be reduced.

Design of enterprise human resources decision support system based on data mining

The management and development of human resources have become one of the core contents of enterprise management. It is from the initial use as an auxiliary tool in human resources business management to the later development of a relatively independent and functional sub-system for the processing of personnel matters between modules, and then to achieve the integration of various sub-systems to form a relatively complete set of human resources information management system. In recent years, with the rapid development of data mining technology, more and more enterprises have begun to attach importance to the assistance of computer information systems to manage the completion of various decision-making activities in operation. This paper designs an enterprise human resource decision support system based on data mining to more effectively support human resource management business information, data standardization, intelligent decision analysis, optimize human resource business processes, improve human resource management business efficiency, provide more effective decision support for enterprise operation and promote enterprise development. Results of the study show the effectiveness of the research.

The Implementation of K-Means Clustering in Coal-fired Power Plant Performance

Performance monitoring for power plant industry is important to know current condition and determined the prior way for improvement strategy. This study aims to classified performance index which are Equivalent Availability Factor (EAF) and Equivalent Forced Outage Rate (EFOR) in various coal-fired power plant in Java Bali, Indonesia. The results of this plants grouping possible to be applied as one of the supports systems in real-time operational decisions to determine plant maintenance priorities. The proposed clustering model is possible to be applied for power plant’s control centre unit in Indonesia in grouping power plants based on performance indexes. The result show that there were 4 clusters of the power plant performance. The clustering drives to identify which plant that must be prioritized for improvement. The clustering method has a model accuracy rate of 87% which indicates that model was valid and applicable. For further research, a decision support system design was required for operational validation. Keywords—Decision-making, Decision support system, Kmeans clustering, Performance, Power plant.

Design of decision support system to identify crop water need

Design of decision support system to identify crop water need

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.

Understanding Clinician Macrocognition to Inform the Design of a Congenital Heart Disease Clinical Decision Support System.

Background and ObjectivesChildren with congenital heart disease (CHD) are at risk of deterioration in the face of common childhood illnesses, and their resuscitation and acute treatment requires guidance of CHD experts. Many children with CHD, however, present to their local emergency departments (ED) with gastrointestinal and respiratory symptoms that closely mimic symptoms of CHD related heart failure. This can lead to incorrect or delayed diagnosis and treatment where CHD expertise is limited. An understanding of the differences in cognitive decision-making processes between CHD experts and ED physicians can inform how best to support ED physicians when treating CHD patients.MethodsCardiac intensivists (CHD experts) and pediatric emergency department physicians (ED physicians) in a major academic cardiac center were interviewed using the critical decision method. Interview transcripts were coded deductively based on Schubert and Klein's macrocognitive frameworks and inductively to allow for new or modified characterization of dimensions.ResultsIn total, 6 CHD experts and 7 ED physicians were interviewed for this study. Although both CHD experts and ED physicians spent a lot of time sensemaking, their approaches to sensemaking differed. CHD experts reported readily recognizing the physiology of complex congenital heart disease and focused primarily on ruling out cardiac causes for the presenting illness. ED physicians reported a delay in attributing the signs and symptoms of the presenting illness to congenital heart disease, because these clinical findings were often non-specific, and thus explored different diagnoses. CHD experts moved quickly to treatment and more time anticipating potential problems and making specific contingency plans, while ED physicians spent more time gathering a range of data prior to arriving at a diagnosis. These findings were then applied to develop a prototype web-based decision support application for patients with CHD.ConclusionThere are differences in the cognitive processes used by CHD experts and ED physicians when managing CHD patients. An understanding of differences in the cognitive processes used by CHD experts and ED physicians can inform the development of potential interventions, such as clinical decision support systems and training pathways, to support decision making pertaining to the acute treatment of pediatric CHD patients.

Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk.

Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.

Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy. Method This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. Conclusion As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.