Intelligent Support System Research Articles

Intelligent financial decision support system based on big data

Abstract In the era of big data, data information has exploded, and all walks of life are impacted by big data. The arrival of big data provides the possibility for the realization of intelligent financial analysis of enterprises. At present, most enterprises’ financial analysis and decision-making based on the analysis results are mainly based on human resources, with poor automation and obvious problems in efficiency and error. In order to help the senior management of enterprises to conduct scientific and effective management, the study uses big data web crawler technology and ETL technology to process data and build an intelligent financial decision support system integrating big data together with Internet plus platform. J Group in S Province is taken as an example to study the effect before and after the application of intelligent financial decision support system. The results show that the crawler technology can monitor the basic data and the big data in the industry in real time, and improve the accuracy of decision-making. Through the intelligent financial decision support system which integrates big data, the core indexes such as profit, net asset return, and accounts receivable of the enterprises can be clearly displayed. The system can query the causes of financial changes hidden behind the financial data. Through the intelligent financial decision support system, it is found that the asset liability ratio, current assets growth rate, operating income growth rate, and financial expenses of J Group are 55.27, 10.38, 20.28%, and 1,974 million RMB, respectively. The growth rate of real sales income of J Group is 0.63%, which is 31.27% less than the excellent value of the industry 31.90%. After adopting the intelligent financial decision support system, the monthly financial statements of the enterprises increase significantly, and the monthly report analysis time decreases. The maximum number of financial statements received by the Group per month is 332, and the processing time at this time is only 2 h. According to the results, it can be seen that the intelligent financial decision support system integrating big data as the research result can effectively improve the financial management level of enterprises, improve the usefulness of financial decision-making, and make practical contributions to the field of corporate financial decision-making.

Intelligent Clinical Decision Support System for Managing COPD Patients.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients' future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work's CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients.

An Intelligent Decision Support System for Naval Logistics

An Intelligent Decision Support System for Naval Logistics

지식 그래프와 설명가능 인공지능 기반 사이버 범죄 수사를 위한 지능형 의사결정 지원 시스템

지식 그래프와 설명가능 인공지능 기반 사이버 범죄 수사를 위한 지능형 의사결정 지원 시스템

The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes

The paper is focused on solving the modern scientific and applied problem related to development and practical use in Decision Support Systems (DSS) of information technologies directed towards forecasting of non-linear non-stationary processes (NNP) that take place in economy and finances as well as in many other areas of activities. Thus, object of study are non-linear non-stationary processes taking place in economy and financial sphere. The basic problem of the study is development of new mathematical models and methods of analysis and forecasting non-linear non-stationary processes in economy and finances, improvement of information decision support technologies that would help to enhance quality of forecast estimates and respective decisions in conditions of uncertainties and risk. The methods given in the paper are used for automating the process of intellectual data analysis that describe the processes under study and automatizing model constructing procedures. As a result of the study performed the information technology was developed to be used in DSS based upon system analysis principles, taking into consideration possible data uncertainties, regression and intellectual data analysis. The technology provides for constructing adequate models of the process under study and computing high quality forecast estimates. The particular feature of the approach proposed is that it provides for high quality of experimental results due to taking into consideration special features of non-linear non-stationary processes that take place in various spheres of activities and their evolution is influenced by many specific factors. The use of the technology proposed in decision support systems of enterprises, state governmental organs, and local self-government will create basis for effective solving the tasks of governing development of non-linear non-stationary processes that take place in many spheres of activities. The approaches proposed in the paper can be used in practice as separately as well as parts of existing information systems at enterprises and organizations.

Modified Bioinspired Method for Decision-Making Support for Prevention and Elimination of the Emergencie's Consequences

The work is devoted to solving the scientific problem of decision support for the prevention and elimination of emergency situations (ES) consequences based on fuzzy logic and machine learning methods. The relevance of this problem is due to the need to optimize the risk of adverse effects on human health and the environment in connection with emergency situations. The increased complexity of the tasks solved within the framework of the designated scientific problem is associated with the presence of information uncertainty in the complex accounting of heterogeneous characteristics, which in some cases can’t be normalized and brought to a single measurement scale. In such conditions, information processes for predicting the occurrence of potentially dangerous chains of events in the technogenic and natural spheres must be built using artificial intelligence methods and fuzzy logic to increase the efficiency of choosing the sequence of actions performed on the available information in order to build the necessary models, methods and algorithms to eliminate negative development situations and ensure monitoring of emergencies potential development cases. The authors give formalized statements of the tasks to be solved. A conceptual data model is proposed for constructing fuzzy decision support rules for the prevention and elimination of emergencies’ consequences. One of the options for formalizing such a data model is the transition to a vector representation of the information space. This will allow in the future to solve the problem of their classification on a set of information elements for distribution by classes of emergency situations. The criterion for evaluating belonging to a certain class is the argument for minimizing the distance between information elements in the vector space. The procedure for the accumulation by an intelligent system of precedent models set for the prevention or elimination of the emergencies consequences, which is a stage of machine learning, is described. After passing it, the intelligent system becomes capable of assessing the semantic similarity of operationally obtained models with precedents that have already fallen into the category of templates. The criterion for evaluating the effectiveness of an intelligent decision support system is the semantic similarity of the operational situational emergency model and the precedent model. A heuristic algorithm for determining semantic proximity was proposed. In order to optimize the time spent on supporting decision-making on the prevention and elimination of the emergency situations consequences, the authors also propose to use decentralized bioinspired methods, the advantages of which are internal procedures that provide diversification of the search space to exit from local optima and quickly obtain quasi-optimal solutions to the problem. The development of a modified bacterial optimization method (MMBO) was described. A software application has been created to conduct a computational experiment. The results of the conducted studies confirmed the advantages of the bacterial optimization proposed modified method.

An Intelligent Support System to Help Teachers Plan Field Trips

Field trips enrich learning programs with out-of-school activities that can bring gains in students’ academic content knowledge and personal growth. However, they are a source of anxiety for teachers because of the bureaucracy, pedagogy, etc., risks they imply. To address this issue, we propose FieldTripOrganizer, a field trip planner based on the mixed-initiative approach aimed at increasing teachers’ autonomy and motivation in designing educational tours. The key aspects of our application are (i) the simultaneous provision of information filtering and automated scheduling support while the user designs the field trip, and (ii) the visual annotation of places and activities to show whether they can be included in the itinerary without violating its time constraints. Different from current tour planners, these functions enable the user to be in full control of the design process, delegating the system to manage difficult and burdensome tasks such as consistency checks and itinerary optimization. We evaluated FieldTripOrganizer in the use case of organizing a science field trip. In a preliminary user study involving 18 science teachers, our application turned up to be superior to a baseline tour planner in both usability and user experience. Moreover, the teachers declared that it was helpful, motivated them, and reduced their anxiety during the design of the field trips.

Agile meeting method for building intelligent decision support systems

In this article, we present an agile method based on a cycle of meetings that guides the construction of intelligent decision support systems. This method presents the phases of initiation, analysis and planning, negotiation, control and intelligent decision support. A cycle represents a passage through all the phases of the method, where as the execution of a phase means that all the planned meetings were held. Each meeting lasted 15 min, and input and output were composed of artifacts that supported the evolution of each meeting. In the initial phase, a meeting was held with everyone with the cards for the survey of the requirements and the construction of the 3D graph to represent the size. In IT meetings, artifacts, forms and tables were used to define the first packages. In the analysis and planning phases, the objectives by key results form were used. In the negotiation, we use the structural sets form. In the control phase, we have the configuration artifact and its control graph. Finally, in intelligent decision support, we use the essential questions form. The method serves as a guide for building intelligent decision support systems that can help with problems like determining whether or not to sign a contract.•In the initial phase, cards for requirement gathering together with a complexity graph and Board Requirements by Layers and Key Person supported the organization of development packages.•In the control phase, the input structures enabled the creation of a continuous control artifact. Furthermore, the control chart showed what is in scope and is part of ongoing control.•The intelligent decision support phase guaranteed the refinement of requirements, which brought intelligence criteria to the development packages and gave them their unique characteristics.

NEURO-FUZZY MODELING OF A DECISION SUPPORT SYSTEM BY A GROUP OF UNMANNED AERIAL VEHICLES UNDER THE INFLUENCE OF THE EXTERNAL ENVIRONMENT

The work discusses the relevance and concept of building an intelligent decision support system based on the use of elements of the theory of fuzzy sets together with the technology of neural networks. The existing requirements for the intellectualization of the control system of a heterogeneous group of unmanned aerial vehicles force developers to switch to the use of artificial intelligence technologies that allow for the training of complex energy-consuming algorithms and ensure their self-organized functioning when solving tasks for their intended purpose. Due to the fact that the process of functioning of a heterogeneous group of unmanned aerial vehicles is described by complex models that take into account the heterogeneity of the group, versatility and multiscenarity when performing tasks for their intended purpose, there is a need to model these processes on a computer. The purpose of the simulation is to obtain a digital version of the double of a real process or object. The probabilistic nature of the description of environmental influences encourages the construction of a model that takes into account all possible behavioral strategies as much as possible and the development of various counteraction scenarios. The most suitable technology capable of analyzing all strategies of the external environment and forming the resulting vector of all situations arising in the process of performing tasks by a group of UAVS for their intended purpose is the well-proven technology of neural networks. However, just getting one solution at the output of the system is not enough, so they resort to modeling situations based on the rule base. Currently, it is easy to form such a base and build a fuzzy model of actions based on it, when the outcomes of alternatives are known inaccurately and the probabilities of their occurrence are estimated using the membership function. Decision-making is based on the method of an ideal point, and the range of acceptable solutions is constructed using a genetic algorithm. The introduction of neural network technology will maximize the viability of the functioning of a group of unmanned aerial vehicles when controlling it both with the help of an operator and offline. The article considers the model of functioning of a group of unmanned aerial vehicles in the conditions of destructive environmental influences.

An Intelligent Clinical Decision Support System based on Attentive Federated Learning

An Intelligent Clinical Decision Support System based on Attentive Federated Learning

An intelligent decision support system for acute postoperative endophthalmitis: design, development and evaluation of a smartphone application

BackgroundToday, clinical decision support systems based on artificial intelligence can significantly help physicians in the correct diagnosis and quick rapid treatment of endophthalmitis as the most important cause of blindness in emergency diseases. This study aimed to design, develop, and evaluate an intelligent decision support system for acute postoperative endophthalmitis.MethodsThis study was conducted in 2020–2021 in three phases: analysis, design and development, and evaluation. The user needs and the features of the system were identified through interviews with end users. Data were analyzed using thematic analysis. The list of clinical signs of acute postoperative endophthalmitis was provided to ophthalmologists for prioritization. 4 algorithms support vector machine, decision tree classifier, k-nearest neighbors, and random forest were used in the design of the computing core of the system for disease diagnosis. The acute postoperative endophthalmitis diagnosis application was developed for using by physicians and patients. Based on the data of 60 acute postoperative endophthalmitis patients, 143 acute postoperative endophthalmitis records and 12 non-acute postoperative endophthalmitis records were identified. The learning process of the algorithm was performed on 70% of the data and 30% of the data was used for evaluation.ResultsThe most important features of the application for physicians were selecting clinical signs and symptoms, predicting diagnosis based on artificial intelligence, physician–patient communication, selecting the appropriate treatment, and easy access to scientific resources. The results of the usability evaluation showed that the application was good with a mean (± SD) score of 7.73 ± 0.53 out of 10.ConclusionA decision support system with accuracy, precision, sensitivity and specificity, negative predictive values, F-measure and area under precision-recall curve 100% was created thanks to widespread participation, the use of clinical specialists' experiences and their awareness of patients' needs, as well as the availability of a comprehensive acute postoperative endophthalmitis clinical dataset.

Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems

Electroencephalograms (EEG) is used to assess patients' clinical records of depression (EEG). The disorder of human thinking is a very complex problem caused by heavy-duty in daily life. We need some future and optimal classifier selection by using different techniques for depression data extraction using EEG. Intelligent decision support is a decision-making process that is automated based on some input information. The primary goal of this proposed work is to create an artificial intelligence-based fuzzy decision support system (AI-FDSS). Based on the given criteria, the AI-FDSS is considered for classifier selection for EEG under depression information. The proposed intelligent decision technique examines classifier alternatives such as Gaussian mixture models (GMM), k-nearest neighbor algorithm (k-NN), Decision tree (DT), Nave Bayes classification (NBC), and Probabilistic neural network (PNN). For analyzing optimal classifiers selection for EEG in depression patients, the proposed technique is criterion-based. First, we develop a general algorithm for intelligent decision systems based on non-linear Diophantine fuzzy numbers to examine the classifier selection technique using various criteria. We use classifier methods to obtain data from depression patients in normal and abnormal situations based on the given criteria. The proposed technique is criterion-based for analyzing optimal classifier selection for EEG in patients suffering from depression. The proposed model for analyzing classifier selection in EEG is compared to existing models.

Intelligent decision support system for optimizing inventory management under stochastic events

Intelligent decision support system for optimizing inventory management under stochastic events

An Intelligent Decision Support System Based on Multi Agent Systems for Business Classification Problem

The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system.

Mathematical and instrumental means of providing an intelligent decision support system for cargo traffic management

In the current geopolitical and economic conditions, the issue of making operational management decisions for the effective functioning of the country's enterprises is particularly acute, taking into account organizational and technological features, as well as increasing levels of risk and uncertainty associated, among other things, with the need to process flows of specific rapidly changing information. In this regard, intelligent decision support systems are becoming increasingly popular, containing modules for collecting, processing and modeling formalized data and having both a universal character and adaptation to a specific range of issues relevant to a certain group or even a single enterprise. The purpose of the presented study was to develop such a system for managing cargo flows. Based on the analysis, the developed intelligent decision support system is proposed, which is used to improve the efficiency of the cargo flow management process. Its main blocks and their logical relationship are described. The mathematical support consists of a complex of classical models adapted to individual modules, as well as developed generalized models of cargo planning and placement. When forming a set of alternative routes, Yandex Maps are used. To automate the selection of the cargo transportation route, algorithms of fuzzy logic rules are used, with the implementation of calculations in the Yandex DataLens environment. The main attention is paid to the subsystem of cargo transportation process management. A generalized functional scheme of digitalization of the formation of waybills and control is presented, as well as a block diagram of the algorithm based on a software product developed and implemented at a number of enterprises that automates the process of filling out waybills, taking into account the specifics of the fleet and selected routes. Within the framework of the synthesis of the structure and algorithm of the intelligent system, which allows to optimize the process of organizing cargo transportation, the approbation of individual proposed modules and approaches was carried out, which showed an increase in the efficiency of forming and choosing routes for organizing cargo flows, reducing the time to find an operational solution, as well as the costs of transportation and control of cargo movement in the conditions of emerging problems associated with accidental factors. The intelligent decision support system is aimed at small firms, as well as enterprises engaged, among other things, also in the organization of cargo transportation by means of their vehicles, since they are in a greater risk zone than large companies whose main economic activity is the provision of cargo transportation services and having a wide network of representative offices, logistics centers and the composition of vehicles.

Proposed Intelligent Decision Support System Using Hedge Algebra Integrated with Picture Fuzzy Relations for Improvement of Decision-Making in Medical Diagnoses

Proposed Intelligent Decision Support System Using Hedge Algebra Integrated with Picture Fuzzy Relations for Improvement of Decision-Making in Medical Diagnoses

Cross-Check QA: A Quality Assurance Workflow to Prevent Missed Diagnoses by Alerting Inadvertent Discordance Between the Radiologist and Artificial Intelligence in the Interpretation of High-Acuity CT Scans

PurposeThe aim of this study was to implement and evaluate a quality assurance (QA) workflow that leverages natural language processing to rapidly resolve inadvertent discordance between radiologists and an artificial intelligence (AI) decision support system (DSS) in the interpretation of high-acuity CT studies when the radiologist does not engage with AI DSS output. MethodsAll consecutive high-acuity adult CT examinations performed in a health system between March 1, 2020, and September 20, 2022, were interpreted alongside an AI DSS (Aidoc) for intracranial hemorrhage, cervical spine fracture, and pulmonary embolus. CT studies were flagged for this QA workflow if they met three criteria: (1) negative results by radiologist report, (2) a high probability of positive results by the AI DSS, and (3) unviewed AI DSS output. In these cases, an automated e-mail notification was sent to our quality team. If discordance was confirmed on secondary review—an initially missed diagnosis—addendum and communication documentation was performed. ResultsOf 111,674 high-acuity CT examinations interpreted alongside the AI DSS over this 2.5-year time period, the frequency of missed diagnoses (intracranial hemorrhage, pulmonary embolus, and cervical spine fracture) uncovered by this workflow was 0.02% (n = 26). Of 12,412 CT studies prioritized as depicting positive findings by the AI DSS, 0.4% (n = 46) were discordant, unengaged, and flagged for QA. Among these discordant cases, 57% (26 of 46) were determined to be true positives. Addendum and communication documentation was performed within 24 hours of the initial report signing in 85% of these cases. ConclusionsInadvertent discordance between radiologists and the AI DSS occurred in a small number of cases. This QA workflow leveraged natural language processing to rapidly detect, notify, and resolve these discrepancies and prevent potential missed diagnoses.

Application of GNSS systems for automated control of working processes of graders with artificial intelligence

Problem. Despite rather successful application of artificial intelligence to control graders, these systems have a number of disadvantages associated with their use: complexity of choosing the system architecture; batch learning and multi-epoch learning require significant time resources; many of the existing computational intelligence systems cannot determine the evolving rules by which the system develops; problems when considering a multitude of indicators that have a complex structure of relationships and contradict each other; difficulty of taking into account the indirect influence of interdependent components in conditions of uncertainty; non-linear character of mutual influence of objects and processes, non-stochastic uncertainty, non-linearity of mutual influence, partial inconsistency and significant interdependence of components. Goal. There is an urgent scientific task of developing a methodology for creating intelligent road vehicle control systems using artificial neural networks and fuzzy cognitive models. Methodology. The methodology of assessment and forecasting in intelligent decision support systems consists of the following sequence of actions: 1) input of initial data. At this stage, the initial data available on the object to be analyzed are entered. Initialization of the basic object state model is carried out; 2) identification of factors and connections between them. Analysis of the models of multi-criteria evaluation of alternatives under conditions of uncertainty showed that the values of model parameters are often represented by intervals, as there are differences of opinion when obtaining parameter values. When there is interval and fuzzy information, it is advisable to use the fuzzy-interval method. Results. This paper briefly examines the features of using neural network technologies, machine control systems and training to increase the productivity of graders. A formalized description of the task of analyzing and forecasting the state of objects in intelligent decision-making support systems of the intelligent system of the road vehicle is carried out. The specified formalization allows us to describe the processes that take place in intelligent decision support systems when solving the tasks of analyzing and forecasting the state of objects of the road machine intelligent system. Originality. An overview of intelligent control systems that can be used for automated control of work processes of earthmoving machines with the use of artificial intelligence is presented, a methodology for implementing practical tasks is proposed. Practical value. An overview of intelligent control systems is presented, practical implementation in programming languages is proposed, the influence of ground conditions on the process of automated work is analyzed, the concept of assessment and forecasting methods in intelligent decision support systems for an intelligent system of graders is given.

ID2S4FH: A Novel Framework of Intelligent Decision Support System for Fire Hazards

Modern societies and industrial sectors are serviced through storage and distribution centres (SDCs) such as supermarkets, malls, warehouses, etc. Large quantities of supplies are stocked here, e.g., food grains, clothes, shoes, pharmaceuticals, electronics, plastics, edible oils, electrical wires/equipment, petroleum products, painting materials, etc. Fires due to the burning of these materials are categorized into six classes, viz., Class A, Class B, Class C, Class D, Class K, and Class F. A fire is extinguished better when the right type of fire retardant is used. A thumb rule on firefighting also says, “never fight a fire if you do not know what is burning”. In this paper, we have proposed an Intelligent Decision Support System (ID2S4FH) to generate a real-time ‘fire-map’ of such SDCs during a fire hazard. We have interfaced six tin-oxide-based gas sensor elements, a temperature and humidity sensor, and a particulate matter (PM) sensor with microcontrollers to capture the real-time signature patterns of the ambient air. We burned sixteen different types of materials belonging to six classes of fire and created a dataset consisting of 2400 samples. The sensor array responses were then pre-processed and analysed using various classifiers trained in different analysis space domains. Among the classifiers, four classifiers achieved ‘all correct’ identification of the fire classes of 80 unknown test samples, and the lowest mean squared error (MSE) achieved was 2.81 × 10−3. During a fire hazard, our proposed ID2S4FH can generate real-time fire maps of SDCs and help firefighters to extinguish the fire using the appropriate fire retardant.

#4345 NOVEL REAL-TIME PREDICTION SYSTEM FOR HYPOTENSION IN DIALYSIS COMBINING RISK INCIDENCE AND NUMERICAL PREDICTION MODEL

Abstract Background and Aims Although there have been many studies on the machine learning prediction model of intradialytic hypotension, the high prediction accuracy in the academic field needs to be transferred to the clinical environment to aid in medical professionals' decision-making process. Before this clinical trial, our team built a data storage, edge computing, and decision support system. However, to achieve real-time decision support, real-time data transmission is also needed along with the system to achieve clinical decision needs. Therefore, this study aims to integrate the AI predictive model with edge computing to build an overall decision support system for intradialytic hypotension in the real-world clinical environment; we have developed a system architecture to deploy the system. Method Development of the IDH decision support system: We developed a complete IDH prediction system to be implemented in dialysis centers and hospitals, which incorporates patient data retrieval to AI prediction models to the web application and monitoring dashboard for medical professionals. The system architecture includes a bridge service used to retrieve the HIS database (patient history), DIS database (dialysis information), and NIS database (nursing care). The data is stored in a data warehouse and then converted into a structured format for AI model training and prediction. Furthermore, an API Server, a communication service for the database and front-end web, is used to present the warning signals in real-time. Lastly, there is a multi-bed monitoring dashboard presenting personalized patient information, the incidence rate of hypotension and numerical prediction of the actual blood pressure value. Design and validation of the online AI prediction model The real-time AI prediction model is built through automatic machine-learning training, which retrains the model every week, utilizing the data of the past 180 days. The features of the input data include biochemistry lab data, nursing records, demographical characteristics, hemodialysis machine parameters and vital signs; it then uses the SMOTE method to fill in missing values and via RFECV to do feature extraction and selection. Lastly, the model is trained through improved RNN models of LSTM (Long short-term memory) and GRU (Gated Recurrent Unit) integrated with machine learning models of XGboost and LightGBM algorithms to generate a combination of binary classification and numerical prediction. Hypotension's output label is defined when systolic blood pressure drops below 90 mmHg. Finally, the validation of the AI machine learning models for binary classification used accuracy score, specificity and sensitivity, whereas the numerical prediction model used RMSE and MAE. Results With the implementation of the edge computing and decision support system, clinical and dialysis data of the patients upload directly to the data warehouse in real-time, and it presents the hypotension prediction information to the medical professional's computer. As a result, the online real-time AI prediction system for the binary classification model yielded an accuracy score, specificity and sensitivity of 0.95, 0.99 and 0.86, respectively, and the RMSE and MAE yielded 17.5 and 11.6, respectively. Furthermore, the model predicted within 15 seconds to ensure a real-time and automatic parameters update. Conclusion Introducing and implementing this integrated intelligence AI prediction system reduces the occurrence of hypotension in dialysis, improves patient safety and satisfaction of patients, and enhances the work environment of medical professionals. Furthermore, this framework and validation can also be provided to home dialysis patients to improve their quality of care. In addition, this system architecture can also be extended to other medical conditions in developing intelligent decision support systems for practical clinical applications.