Traditional Decision Support Systems Research Articles

Research on the Application Framework of Generative AI in Emergency Response Decision Support Systems for Emergencies

With the rapid development of artificial intelligence technology, generative AI has shown broad application prospects and potential in various fields. Frequent emergencies have put forward higher requirements for traditional Emergency Response Decision Support Systems (ERDSS). This paper proposes a theoretical framework of ERDSS based on generative AI (ERDSS-GAI), which deeply integrates generative AI with three stages of emergency response decision-making. The framework aims to leverage the advantages of generative AI in massive data processing, knowledge mining, strategy optimization, and other aspects, thereby enhancing the intelligence level and emergency response capability of ERDSS. The key components and implementation path of ERDSS-GAI are systematically explained from a theoretical perspective, and its application value is analyzed through the case of rainstorm and flood disaster in Shenzhen. This research demonstrates that generative AI can improve the scientific and refined level of emergency response decision-making, providing a theoretical framework and practical insights for its widespread adoption in emergency management.

Enhancing Medication Safety with ML-Enhanced Decision Support Systems: A Comparative Analysis of Prescription Error Detection

Background: Medication prescription errors are a global issue, leading to significant morbidity and mortality. Traditional rule-based Medical Decision Support Systems (MDSS) are often ineffective, generating numerous false alerts and failing to detect all potential errors. This study assesses a new anomaly detection system integrated with Electronic Health Records (EHR) to improve the accuracy and utility of medication error warnings. Methods: Anomalous prescription detection was implemented alongside an existing MDSS in a real-world inpatient setting over 18 months. The new system utilized Machine Learning (ML) combined with a rule-based MDSS to analyze historical EHR data. It aimed to identify and flag high-risk prescriptions through real-time anomaly detection. The performance of this hybrid system was compared against traditional MDSS and multicriteria query (MQ) methods. A clinical pharmacist reviewed 415 patients (3401 prescriptions) to validate the effectiveness of the system, assessing notifications for accuracy, clinical relevance, and practicality. Results: The ML-enhanced MDSS demonstrated superior performance compared to traditional systems. It achieved a 76% interception rate for prescriptions needing pharmacist review and a precision rate of 75%. The hybrid system outperformed traditional MDSS and MQ methods, with areas under the ROC and PRC curves of 0.84 and 0.79, respectively, compared to 0.66 and 0.57 for MDSS and 0.7 and 0.58 for MQ approaches. Conclusion: Integrating ML with rule-based MDSS significantly improves the detection of high-risk medication prescriptions, reducing false alerts and enhancing accuracy. This hybrid approach offers a more effective tool for identifying potential medication errors and improving patient safety in inpatient settings.

On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models

Objective:Traditional knowledge-based and machine learning diagnostic decision support systems have benefited from integrating the medical domain knowledge encoded in the Unified Medical Language System (UMLS). The emergence of Large Language Models (LLMs) to supplant traditional systems poses questions of the quality and extent of the medical knowledge in the models’ internal knowledge representations and the need for external knowledge sources. The objective of this study is three-fold: to probe the diagnosis-related medical knowledge of popular LLMs, to examine the benefit of providing the UMLS knowledge to LLMs (grounding the diagnosis predictions), and to evaluate the correlations between human judgments and the UMLS-based metrics for generations by LLMs. Methods:We evaluated diagnoses generated by LLMs from consumer health questions and daily care notes in the electronic health records using the ConsumerQA and Problem Summarization datasets. Probing LLMs for the UMLS knowledge was performed by prompting the LLM to complete the diagnosis-related UMLS knowledge paths. Grounding the predictions was examined in an approach that integrated the UMLS graph paths and clinical notes in prompting the LLMs. The results were compared to prompting without the UMLS paths. The final experiments examined the alignment of different evaluation metrics, UMLS-based and non-UMLS, with human expert evaluation. Results:In probing the UMLS knowledge, GPT-3.5 significantly outperformed Llama2 and a simple baseline yielding an F1 score of 10.9% in completing one-hop UMLS paths for a given concept. Grounding diagnosis predictions with the UMLS paths improved the results for both models on both tasks, with the highest improvement (4%) in SapBERT score. There was a weak correlation between the widely used evaluation metrics (ROUGE and SapBERT) and human judgments. Conclusion:We found that while popular LLMs contain some medical knowledge in their internal representations, augmentation with the UMLS knowledge provides performance gains around diagnosis generation. The UMLS needs to be tailored for the task to improve the LLMs predictions. Finding evaluation metrics that are aligned with human judgments better than the traditional ROUGE and BERT-based scores remains an open research question.

Artificial intelligence in routine blood tests

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.

Design principles for artificial intelligence-augmented decision making: An action design research study

ABSTRACT Artificial intelligence (AI) applications have proliferated, garnering significant interest among information systems (IS) scholars. AI-powered analytics, promising effective and low-cost decision augmentation, has become a ubiquitous aspect of contemporary organisations. Unlike traditional decision support systems (DSS) designed to support decisionmakers with fixed decision rules and models that often generate stable outcomes and rely on human agentic primacy, AI systems learn, adapt, and act autonomously, demanding recognition of IS agency within AI-augmented decision making (AIADM) systems. Given this fundamental shift in DSS; its influence on autonomy, responsibility, and accountability in decision making within organisations; the increasing regulatory and ethical concerns about AI use; and the corresponding risks of stochastic outputs, the extrapolation of prescriptive design knowledge from conventional DSS to AIADM is problematic. Hence, novel design principles incorporating contextual idiosyncrasies and practice-based domain knowledge are needed to overcome unprecedented challenges when adopting AIADM. To this end, we conduct an action design research (ADR) study within an e-commerce company specialising in producing and selling clothing. We develop an AIADM system to support marketing, consumer engagement, and product design decisions. Our work contributes to theory and practice with a set of actionable design principles to guide AIADM system design and deployment.

Adopting Artificial Intelligence in a Decision Support System – Learnings from Comment Moderation Systems

Enterprise Information Systems (EIS) comprise components for decision-making in an organization. While traditional Decision Support Systems (DSS) rely on sophistically designed decision models, this approach has its limitations when it comes to making decisions based on large amounts of unstructured data. In this paper, we present the case of online community management, where moderators need to decide if user content (i.e., comments) can be published. We have implemented a moderation platform that utilizes Natural Language Processing (NLP) and Machine Learning (ML) to support moderators in their decision-making. From the development process and adoption of our platform, which were carried out as a Design Science Research (DSR) project, we have derived six design principles that assist in designing ML-based DSS. Our results imply that an ML-based DSS should be implemented using an open, customizable system, where decisions are made transparent and interpretable to users. Users need special onboarding and should always have the possibility to overrule the system.

FUZZY LOGIC SYSTEMS WITH DATA CLASSIFICATION - A COOPERATIVE APPROACH FOR INTELLIGENT DECISION SUPPORT

In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets.

Community Governance Decision Based on Knowledge Map

The participation degree of community residents has become a yardstick to measure the community governance system and governance capacity in China. Since 2000, China has vigorously promoted community construction. The related research literature on community construction in China is also showing an explosive growth trend. Knowledge map refers to the analysis of complex domain knowledge through data mining, information processing, knowledge measurement, chart drawing, etc., so as to achieve the purpose of understanding the dynamic development law of knowledge domain through diagrams. In the face of complex knowledge fields, we can use knowledge maps to combine various information to obtain relational networks and analyze problems from the perspective of “relationships.” Knowledge map is widely used in intelligent search, intelligent question and answer, and other fields. Using the intelligent decision support framework based on knowledge map can help us more clearly find the problems existing in the traditional decision support system and provide corresponding solutions. Combining big data, knowledge map, and other large-scale knowledge analysis and modeling technologies with decision support systems can strengthen the ability of problem analysis and processing and form a knowledge and relationship network. This paper provides direction and theoretical support for the research and development of community construction and governance in China in the future.

Distribution Network Disaster Early Warning and Production Decision Support System Based on Multisource Data

Aiming at the problems of long warning time and low warning accuracy in the traditional distribution network disaster early warning and production decision support systems, a distribution network disaster early warning and production decision support system based on multisource data is designed. The forecast information is collected through the data collector, the wind load and lightning trip rate of the line are calculated, all of the information is integrated together for multisource data fusion processing, and the distribution network disaster early warning model is constructed in accordance with the system hardware, which is designed with a data collector, gateway, man‐machine interface, fault analysis module, disaster early warning module, and expert decision support module. According to the system hardware and software design, the design of a distribution network disaster early warning and production decision support system based on multisource data is realized. The simulation results show that the system has high accuracy and a short warning time.

Shared clinical decision-making on vaccines: out of sight, out of mind.

Clinical decision support systems, which provide automated reminders in electronic health systems, are designed to provide physicians and other health professionals support in clinical decision-making. New clinical guidance from the Advisory Committee on Immunization Practices on a new category of recommendations referred to as "shared clinical decision-making" have left providers struggling to interpret how to best implement recommendations for adult vaccines. The issue at hand is how to ensure that a conversation between the patient and provider occurs. While traditional clinical decision support systems have driven immunization for years, these systems support a binary default opt-in process. The goal for shared decision-making is the discussion rather than the vaccination. The recommended provider-patient conversations need to be supported with both provider guidance as well as tools to ensure vaccines are not omitted from the conversations, particularly as future vaccine candidates progress through the vaccine development pipeline.

Improving Intelligent Decision Making in Urban Planning

Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support the decision process. This paper aims to elaborate an intelligent decision support system (IDSS) that provides relevant assistance to urban planners in urban projects. This research addresses the use of new techniques that contribute to intelligent decision making: machine learning classifiers, naïve Bayes classifier, and agglomerative clustering. Finally, a prototype is being developed to concretize the proposition.

Research on Enterprise Economic Management System Based on Computer Big Data Technology

The support system for enterprise economic management is an intelligent decision support system. It is composed of user interface management system, problem handling system, database and database management system, model library and model library management system, method library and method library management system, knowledge base and knowledge base management system. It integrates economic mathematical modelling, artificial intelligence technology, operations research methods and decision-making methods. Through the combination of knowledge reasoning and numerical calculation, it provides more powerful decision support capabilities than traditional decision support systems. Based on this research background, the paper takes financial management in economic management as an entry point and introduces a computer financial economic management system based on mobile Agent. At the same time, the article introduces a financial and economic early warning algorithm for enterprises based on decision trees.

Research on the Development of an Intelligent Financial Management System Based on Data Mining Technology

With the in-depth development of enterprise information construction, enterprises accumulate more and more business data, at the same time, the constantly changing market environment and fierce competition, have prompted managers to more in-depth information desire. An intelligent financial decision support system is a specific application in the financial field after the integration of traditional decision support system and expert system in artificial intelligence. The application of an intelligent financial decision support system can help enterprises to classify, sort, process and analyze data, and transform historical data that cannot be further used into knowledge base information for senior decision makers. However, in most of the existing researches on an intelligent financial decision support system, the application of data mining technology is not specific enough, and only gives the construction of a model library and knowledge base in a general way. At the same time, it is seldom combined with the actual situation of enterprises, so the operability is not strong.

Research on investment decision-making model from the perspective of “Internet of Things + Big data”

With the continuous improvement of the global securities market, the market competition is unprecedented fierce. In the aspect of investment decision support system, there is an urgent need to continuously absorb new information processing technologies and improve the scientific and standardization of decision-making, so as to achieve the goal of improving investment decision-making efficiency and stabilizing investment returns. Firstly, this paper introduces the defect that the database, model base and knowledge base are designed and implemented independently in the traditional decision support system. Then, constructing a unified and efficient data processing platform for Internet of Things based on DMFS technology, which realizes the integration of database and model, and innovatively establishes a data mining model facing market big data, dynamically analyzes and proposes investment decisions. Finally, the operating income data of a listed company in recent ten years are selected for simulation, which verifies the efficiency of the model system in processing dynamic data and the stability of investment income.

Expert assessment systems to support decision-making for sustainable development of complex technological and socio-economic facilities

In this paper, we investigate problems of decision making in management systems for the sustainable development of complex technological and socio-economic facilities. We show both the limitations of traditional expert systems and decision support systems, and the necessity of using expert evaluation technologies to find possible development strategies. Based on that we substantiate the need of creating a new class of systems, i.e. Automated eXpert Assessment Systems, and propose their organizational structure and design principles. We substantiate the level of automation of the work performed during the examinations and describe the composition of models and computer programs we recommend for creating effective automated expert assessment systems and corresponding technology. In the paper, we give examples of using the proposed method for various areas of human activity, in the management of urban infrastructure and e-learning at the universities, and show the effectiveness of the developed approach.

Intelligent Campus Implementation For Smart Cities

Digital transformation has provided the decision-making infrastructure needed to produce custom solutions for each person. Traditional decision support systems have begun to evolve into intelligent systems and mass customization has begun to be applied in many areas of life, such as education, transportation, health, and production. In order to achieve digital transformation in teaching, it is necessary not only to focus on the teaching process but also to focus on environmental factors. The provision of digital content and the performance of the student are carried out in the digital environment with education technologies. It is aimed to maintain sustainability of the environmental quality by following the digital environment by means of digital transformation and to improve the teaching processes with intelligent systems that will interfere immediately in emergency situations.

Dosage Prediction in Pediatric Medication Leveraging Prescription Big Data

Rational use of medicines is of great importance in pediatric clinical medication. As the pharmacokinetics and pharmacodynamics of the pediatric group is highly dynamic, it is a great challenge to determine the rational dosage for pediatric patients. Traditional clinical decision support systems for dosage guidance largely rely on manual collection of medication information, which usually suffers from incomplete and missing evidences for the pediatric group. In this paper, we propose a data-driven approach to accurately predict pediatric medication dosages by leveraging prescription big data. More specifically, we first identify two relevant factors of pediatric medication dosage, i.e., the physiology factors including patients' body weight and age group, and the indication factors that affect clinical dosage patterns. We then extract the corresponding physiology and indication features, and propose a hybrid-learning-based method to adaptively integrate the two sets of heterogeneous features into a model for accurate pediatric dosage prediction. We evaluate our method on real-world prescription datasets from two tertiary children's hospitals. Results show that our method predicts pediatric medication dosages with an accuracy above 81.3%, and consistently outperforms other baselines.

An Overview of Functional Components of Artificial Intelligence Financial Decision Support System

The development of artificial intelligence technology makes the intelligent decision-making of enterprise finance possible. Combined with the traditional enterprise financial decision support system, this paper summarizes and introduces the structure and function of the enterprise Financial Decision Support System and its subsystems under the development of artificial intelligence in detail.

Moving Beyond Traditional Decision Support Systems

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.

An Intelligent and Electronic System based Classification and Prediction for Heart Disease Diagnosis

The healthcare industry collects massive amounts of healthcare data which, unfortunately, are not “mined” to discover hidden for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype An Intelligent System based Classification and Prediction for Heart Disease Diagnosis using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. An Intelligent System based Classification and Prediction for Heart Disease Diagnosis using data mining techniques can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, L.V and Ejection Fraction it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease. Index Termsـــــــ Data Mining, Ejection Fraction, Heart Disease, Decision Support System, Classification Techniques, intelligent system. INTRODUCTION Data Mining is a non-trivial extraction of implicit, previously unknown and potential useful about data [1][13] . In short, it is a process of analyzing data from different Perspective and gathering the knowledge from it, the discovered knowledge can be used for different applications for example healthcare industry. The health care industry is generally information rich, which is not feasible to handle manually. These large amounts of data are very important in the field of data mining to extract useful and generate relationships amongst the attributes. Heart disease Diagnosis is a complex task which requires much Experience and knowledge. In the health care industry the data mining is mainly used for predicting the diseases from the datasets. The data mining techniques, namely decision trees, Naive bayes, and neural networks are analyzed on heart disease database [2] .Medical practitioners generate data with a wealth of hidden present. For classification and prediction, unused data must be converted into a dataset for modeling using different data mining methods. In the proposed work, an intelligent system based classification and