Development Of Decision Support System Research Articles

Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers

The objective of this study is to utilize artificial intelligence techniques for the diagnosis of complications and diseases that may arise after liver transplantation, as well as for the identification of patients in need of transplantation. To achieve this, an interface was developed to collect patient information from Atatürk University Research Hospital, specifically focusing on individuals who have undergone liver transplantation. The collected data were subsequently entered into a comprehensive database. Additionally, relevant patient information was obtained through the hospital’s information processing system, which was used to create a data pool. The classification of data was based on four dependent variables, namely, the presence or absence of death (“exitus”), recurrence location, tumor recurrence, and cause of death. Techniques such as Principal Component Analysis and Linear Discriminant Analysis (LDA) were employed to enhance the performance of the models. Among the various methods employed, the LDA method consistently yielded superior results in terms of accuracy during k-fold cross-validation. Following k-fold cross-validation, the model achieved the highest accuracy of 98% for the dependent variable “exitus”. For the dependent variable “recurrence location”, the highest accuracy obtained after k-fold cross-validation was 91%. Furthermore, the highest accuracy of 99% was achieved for both the dependent variables “tumor recurrence” and “cause of death” after k-fold cross-validation.

Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions

BackgroundPost bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for the development of a decision support system (DSS) that can warn individuals about upcoming PBH events, thus enabling preventive actions to avoid impending episodes. In view of this, we developed various algorithms based on linear and deep learning models to forecast PBH episodes in the short-term.MethodsWe leveraged a dataset obtained from 50 patients with PBH after Roux-en-Y gastric bypass, monitored for up to 50 days under unrestricted real-life conditions. Algorithms’ performance was assessed by measuring Precision, Recall, F1-score, False-alarms-per-day and Time Gain (TG).ResultsThe run-to-run forecasting algorithm based on recursive autoregressive model (rAR) outperformed the other techniques, achieving Precision of 64.38%, Recall of 84.43%, F1-score of 73.06%, a median TG of 10 min and 1 false alarm every 6 days. More complex deep learning models demonstrated similar median TG but inferior forecasting capabilities with F1-score ranging from 54.88% to 64.10%.ConclusionsReal-time forecasting of PBH events using CGM data as a single input imposes high demands on various types of prediction algorithms, with CGM data noise and rapid postprandial glucose dynamics representing the key challenges. In this study, the run-to-run rAR yielded most satisfactory results with accurate PBH event predictive capacity and few false alarms, thereby indicating potential for the development of DSS for people with PBH.

A web-based decision support system to support occupational therapy services in promoting active ageing

Background: Dealing with the consequences of Thailand’s ageing population is a significant challenge. The growing usage of Information and Communication Technology (ICT) has the potential to support healthy and active ageing. Objective: This study aimed to develop a web-based Decision Support System (DSS) and evaluate its usability. Materials and methods: Research and Development (R&D) was a study design. It was divided into two phases: the development of the web-based DSS and its usability. Results: The web-based DSS was designed under the requirements of the users and comprised seven components: 1) the login page; 2) the main page; 3) the register page; 4) active ageing measurement; 5) decision-making for older people; 6) the Occupational Therapy (OT) recommendations system; and 7) exporting information. A System Usability Score (SUS) of 80.41 in the usability test showed that the web-based DSS was very user-friendly and had excellent efficacy, efficiency, and satisfaction. Conclusion: The web-based DSS effectively enhanced ageing for older people.

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Structural model of decision support system for sustainable development of oil and gas companies

Structural model of decision support system for sustainable development of oil and gas companies

Decision Support System for Socio-Economic Development of Rural Settlements: General Provisions

Decision Support System for Socio-Economic Development of Rural Settlements: General Provisions

IMPLEMENTATION OF MSME CREDIT LOAN DETERMINATION USING MACHINE LEARNING TECHNOLOGY WITH KNN (K-NEAREST NEIGHBORS) ALGORITHM

This research aims to develop a loan eligibility prediction model for Micro, Small, and Medium Enterprises (MSMEs) using the K-Nearest Neighbors (KNN) algorithm. The dataset utilized includes variables such as the length of business operation, number of workers, assets, and net turnover of MSMEs. The data is split into training and test sets with a 70:30 ratio. The KNN model is trained using the training data to classify loan eligibility based on a specified k value. The model predictions include whether a loan is accepted and the probability associated with each decision. The results indicate that the KNN model achieved an accuracy rate of 83.939% in predicting loan eligibility. Based on the predictions, 929 MSMEs were deemed eligible to receive loans according to the KNN model recommendations, while 170 MSMEs were classified as ineligible. These findings contribute significantly to the development of decision support systems in the banking and finance sectors, particularly in evaluating MSME loan eligibility.

A novel case-based reasoning system for explainable lung cancer diagnosis.

Lung cancer is a leading cause of cancer death worldwide. The survival rate is generally higher when this disease is detected in its early stages. Advances in artificial intelligence (AI) have enabled the development of decision support systems that help physicians diagnose diseases. However, these systems often provide final predictions without clarifying how those decisions are reached, raising concerns about trust and adaptation in life-threatening diseases. To address these issues, this study proposes an explainable case-based reasoning (XCBR) approach that considers both physicians' tendency to base their decisions on past cases and the case complexity in its predictions and explanations. The proposed XCBR is enhanced with naïve Bayes (NB) and multilayer perceptron (MLP) classifiers which are processed hierarchically: when the NB deems its predictions to be unlikely, the MLP classifier is employed to verify or update the predictions. This approach incorporates Shapley additive explanations values to elucidate the solutions offered by the MLP. Furthermore, it utilizes the Harris hawks optimization algorithm for feature selection and feature weighting. The proposed XCBR achieved high accuracies of 94.47% and 100% on two different datasets, demonstrating its generalization capability. Based on Wilcoxon signed-rank test, its classification accuracy is comparable to that of other state-of-the-art approaches and commonly used classifiers. Moreover, since this approach prioritizes case complexity in its predictions and explanations, it offers better explainability and is particularly suited for serious diseases.

Uncertainty-aware automatic TNM staging classification for [18F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning

Background[18F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text. Accordingly, we developed a multi-task ‘TNMu’ classifier to classify the presence/absence of tumour, node, metastasis (‘TNM’) findings (as defined by The Eight Edition of TNM Staging for Lung Cancer). This is combined with an uncertainty classification task (‘u’) to account for studies with ambiguous TNM status.Methods2498 reports were annotated by a nuclear medicine physician and split into train, validation, and test datasets. For additional evaluation an external dataset (n = 461 reports) was created, and annotated by two nuclear medicine physicians with agreement reached on all examples. We trained and evaluated eleven publicly available PLMs to determine which is most effective for PET-CT reports, and compared multi-task, single task and traditional machine learning approaches.ResultsWe find that a multi-task approach with GatorTron as PLM achieves the best performance, with an overall accuracy (all four tasks correct) of 84% and a Hamming loss of 0.05 on the internal test dataset, and 79% and 0.07 on the external test dataset. Performance on the individual TNM tasks approached expert performance with macro average F1 scores of 0.91, 0.95 and 0.90 respectively on external data. For uncertainty an F1 of 0.77 is achieved.ConclusionsOur ‘TNMu’ classifier successfully extracts TNM staging information from internal and external PET-CT reports. We concluded that multi-task approaches result in the best performance, and better computational efficiency over single task PLM approaches. We believe these models can improve PET-CT services by assisting in auditing, creating research cohorts, and developing decision support systems. Our approach to handling uncertainty represents a novel first step but has room for further refinement.

Development and Evaluation of a GPT4-Based Orofacial Pain Clinical Decision Support System.

Background: Orofacial pain (OFP) encompasses a complex array of conditions affecting the face, mouth, and jaws, often leading to significant diagnostic challenges and high rates of misdiagnosis. Artificial intelligence, particularly large language models like GPT4 (OpenAI, San Francisco, CA, USA), offers potential as a diagnostic aid in healthcare settings. Objective: To evaluate the diagnostic accuracy of GPT4 in OFP cases as a clinical decision support system (CDSS) and compare its performance against treating clinicians, expert evaluators, medical students, and general practitioners. Methods: A total of 100 anonymized patient case descriptions involving diverse OFP conditions were collected. GPT4 was prompted to generate primary and differential diagnoses for each case using the International Classification of Orofacial Pain (ICOP) criteria. Diagnoses were compared to gold-standard diagnoses established by treating clinicians, and a scoring system was used to assess accuracy at three hierarchical ICOP levels. A subset of 24 cases was also evaluated by two clinical experts, two final-year medical students, and two general practitioners for comparative analysis. Diagnostic performance and interrater reliability were calculated. Results: GPT4 achieved the highest accuracy level (ICOP level 3) in 38% of cases, with an overall diagnostic performance score of 157 out of 300 points (52%). The model provided accurate differential diagnoses in 80% of cases (400 out of 500 points). In the subset of 24 cases, the model's performance was comparable to non-expert human evaluators but was surpassed by clinical experts, who correctly diagnosed 54% of cases at level 3. GPT4 demonstrated high accuracy in specific categories, correctly diagnosing 81% of trigeminal neuralgia cases at level 3. Interrater reliability between GPT4 and human evaluators was low (κ = 0.219, p < 0.001), indicating variability in diagnostic agreement. Conclusions: GPT4 shows promise as a CDSS for OFP by improving diagnostic accuracy and offering structured differential diagnoses. While not yet outperforming expert clinicians, GPT4 can augment diagnostic workflows, particularly in primary care or educational settings. Effective integration into clinical practice requires adherence to rigorous guidelines, thorough validation, and ongoing professional oversight to ensure patient safety and diagnostic reliability.

A Machine Learning-Based Decision Support System for the Prognostication of Neurological Outcomes in Successfully Resuscitated Out-of-Hospital Cardiac Arrest Patients.

Background/Objectives: This study uses machine learning and multicenter registry data for analyzing the determinants of a favorable neurological outcome in patients with out-of-hospital cardiac arrest (OHCA) and developing decision support systems for various subgroups. Methods: The data came from the Korean Cardiac Arrest Research Consortium registry, with 2679 patients who underwent OHCA aged 18 or above with the return of spontaneous circulation (ROSC). The dependent variable was a favorable neurological outcome (Cerebral Performance Category score 1-2), and 68 independent variables were included, e.g., first monitored rhythm, in-hospital cardiopulmonary resuscitation (CPR) duration and post-ROSC pH. A random forest was used for identifying the major determinants of the favorable neurological outcome and developing decision support systems for the various subgroups stratified by the major variables. Results: Based on the random forest variable importance, the major determinants of the OHCA patient outcomes were the in-hospital CPR duration (0.0824), in-hospital electrocardiogram on emergency room arrival (0.0692), post-ROSC pH (0.0579), prehospital ROSC before emergency room arrival (0.0565), coronary angiography (0.0527), age (0.0415), first monitored rhythm (EMS) (0.0402), first monitored rhythm (community) (0.0401), early coronary angiography within 24 h (0.0304) and time from scene arrival to CPR stop (0.0301). It was also found that the patients could be divided into six subgroups in terms of their prehospital ROSC and first monitored rhythm (EMS), and that a decision tree could be developed as a decision support system for each subgroup to find the effective cut-off points regarding the in-hospital CPR duration, post-ROSC pH, age and hemoglobin. Conclusions: We identified the major determinants of favorable neurological outcomes in successfully resuscitated patients who underwent OHCA using machine learning. This study demonstrates the strengths of a random forest as an effective decision support system for each stratified subgroup (prehospital ROSC and first monitored rhythm by EMS) to find its own optimal cut-off points for the major in-hospital variables (in-hospital CPR duration, post-ROSC pH, age and hemoglobin).

Development of an Autonomous Drone-Based Irrigation Decision Support System Utilizing Image Processing and Machine Learning Techniques

Efficient management of water resources is essential for sustaining the global food supply amidst growing populations and climate change. Traditional irrigation methods are often plagued by inefficiencies, leading to significant water wastage. This paper presents the development and validation of an autonomous drone-based irrigation system that leverages advanced image processing and machine learning techniques to optimize water usage in agriculture. The system employs standard low-cost cameras to capture high-resolution aerial images, which are processed to accurately predict the water needs of the plants and inform irrigation decisions in real-time also it can do autonomous watering by controlling the electrical water valve in the specified irrigation areas. Comprehensive field tests conducted on pepper crops demonstrate the system's ability to enhance water use efficiency and improve crop yields. By integrating state-of-the-art technologies such as TensorFlow techniques for machine lear-nig, image analysis and autonomous navigation capabilities, the proposed solution represents a significant advancement in precision agriculture. The results indicate that the autonomous drone-based irrigation system can substantially reduce water consumption while maintaining or enhancing crop productivity, thereby promoting sustainable agricultural practices.

TIRADS-based artificial intelligence systems for ultrasound images of thyroid nodules: protocol for a systematic review.

The thyroid imaging reporting and data system (TIRADS) was developed as a standard global term to describe thyroid nodule risk features, aiming to address issues such as variability and low reproducibility in nodule feature detection and interpretation by different physicians. The objective of this study is to comprehensively study articles that utilize AI techniques to design and develop decision support systems for classifying thyroid nodule risk on the basis of various TIRADS guidelines from ultrasound images. This protocol includes five steps: identification of key research questions of the review, descriptions of the systematic literature search strategies, criteria for study inclusion and exclusion, study quality measures, and the data extraction process. We designed a complete search string using PubMed, Scopus, and Web of Sciences to retrieve all relevant English language studies up to January 2024. A PRISMA diagram was constructed, inclusion and exclusion criteria were defined, and after a quality assessment of the included papers, relevant data were extracted. The protocol of this systematic review was registered in the PROSPERO database (CRD42024551311). We anticipate that our findings will assist researchers in creating higher-quality systems with increased efficiency, reducing unnecessary biopsies, improving the reproducibility and reliability of thyroid nodule diagnostics, and providing good educational opportunities for less experienced physicians. In this study, a protocol was used for performing a systematic review to evaluate the diagnostic performance and other various aspects used in the design and development of artificial intelligence CAD systems based on various thyroid imaging reporting and data systems (TI-RADSs).

Development of Sensor and Decision Support System (DSS)-based Automated Irrigation System for Enhancing Water Productivity of Tomato Crop

Development of Sensor and Decision Support System (DSS)-based Automated Irrigation System for Enhancing Water Productivity of Tomato Crop

Assessing the impact of vaccines on COVID-19 efficacy in survival rates: a survival analysis approach for clinical decision support.

The global COVID-19 pandemic, caused by the SARS-CoV-2 virus, has presented significant challenges to healthcare systems worldwide. This study, based on an analysis of a cohort from the Public Health System of Andalusia (Spain), aims to evaluate how vaccination affects case-fatality rate in patients hospitalized due to COVID-19 infection in Andalusia. The cohort consists of 37,274 individuals after applying the inclusion criteria. We conducted survival analyses employing the Cox proportional hazards models and generated adjusted survival curves to examine the outcomes. The analyses were performed from three perspectives: vaccinated vs. unvaccinated patients, vaccinated and unvaccinated patients grouped by age, and stratified by vaccination status. Results indicate a substantial correlation between vaccination and a 20% reduction in the risk of case-fatality. Age-specific effects reveal varying degrees of protection across different age groups. These findings emphasize the pivotal role of vaccination status in COVID-19 risk assessment, supporting the development of a clinical decision support system for accurate predictions and optimizing healthcare management at admission.

Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS.

Analysis of the Impact of Operatorʼs Activity on the Control System of Unmanned Aerial Vehiclesles Analysis

The article describes the results of the experimental study on the influence of operator’s activities on the control system of a heterogeneous unmanned aerial vehicles’ group. The proposed mathematical apparatus allows to present the actions of the operator as part of the automated control system, to carry out a quantitative assessment of the efficiency of the operator’s actions. It also allows to evaluate the impact of the results of this activity on the effectiveness of solving tasks in the control system of an unmanned aerial vehicles’ group. The given experimental data and obtained laws of distribution of various random variables can be used in modelling the activity of the operator in complex control systems of an unmanned aerial vehicles’ group. Management of operators’ activity models enables to improve the quality of the development of decision support system.

Decision Support System for the Design Process of Apatite Biopolymer Composite Parts

Abstract In connection with the increase in the number and severity of various types of bone tissue injuries received as a result of wounds during military operations in Ukraine, an important issue in orthopedics and traumatology is making informed decisions about the possibility of restoring the integrity and functions of bone tissue when using different types of composition, porosity and strength of apatite-biopolymer composites. The scientific direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization and training is proposed. The new knowledge obtained as a result of the project will become the necessary basis for making optimal decisions in practice for the introduction of the latest methods of treatment and prosthetics in trauma surgery, oncology, cranio-maxillofacial surgery, dentistry, taking into account the risks of biocompatibility of apatite-biopolymer composites. Software development of an intelligent decision support system will be used to design bone substitutes with controlled composition, structure, porosity and mechanical strength for the further selection of additive technology for its production from apatite-polymer composites, which will contribute to increasing the efficiency of treatment and prosthetics in orthopedics and traumatology.

The Role of Cognitive Bias in Principal Decision Making: A Narrative Analysis of the Literature

This study aims to critically examine the role of cognitive biases in principal decision-making processes by synthesizing and analysing existing literature. It seeks to identify key cognitive biases affecting principal decisions, their impact on organizational outcomes, and potential mitigation strategies. A comprehensive narrative analysis of the literature was conducted. Relevant peer-reviewed articles, books, and book chapters published between 1970 and 2023 were systematically identified and reviewed using major academic databases such as Web of Science, JSTOR, and Google Scholar. The analysis revealed that cognitive biases, particularly confirmation bias, overconfidence, anchoring, and availability bias, significantly influence principal decision-making. These biases can lead to suboptimal decisions, affecting strategic planning, risk assessment, and overall organizational performance. The study also identified several debiasing techniques, including awareness training and structured decision-making frameworks, which show promise in mitigating the impact of cognitive biases. This study provides a comprehensive and up-to-date synthesis of research on cognitive biases in principal decision-making, offering a holistic view of the field’s current state. It uniquely combines insights from cognitive psychology, behavioural economics, and management science to provide a multidisciplinary perspective on the topic. The study contributes to both theory and practice by offering a structured understanding of how cognitive biases influence principal decision-making. It provides valuable insights for principals and organizations to improve their decision-making processes and highlights areas for future research in this critical field. The findings can inform the development of more effective principal training programs and decision support systems.

Анализ и классификация многокритериальных методов принятия решений

Decision-making requires in-depth analysis through the evaluation and comparison of various alternative solutions. These tasks are characterized by multiple criteria used for comparison. The variability of parameters and conditions in decision-making has led to the development of numerous methods aimed at facilitating this process effectively. Currently, several hundred multi-criteria decision-making methods are known, making it challenging to select the most appropriate one for practical use. Therefore, research on the analysis, classification, and systematization of multi-criteria decision-making methods is relevant and essential for decision-makers and developers of decision support systems. Most classifications emphasize the methodologies and purposes of these methods, often overlooking their inherent limitations. This article attempts to systematize the main multi-criteria decision-making methods, considering their limitations. It analyzes the most widely used methods and their classifications. Based on identified limitations, the paper proposes a classification of multi-criteria decision-making methods and guidelines for selecting them. Applying this approach can enhance the validity of method selection and, consequently, the effectiveness of the decisions made.