Decision-making Process Research Articles

Enhancing Predictive Maintenance of Power Transformers Through Machine Learning Approaches

This paper focuses on the use of machine learning algorithms to assist in predictive maintenance, aiming to reduce downtime and associated costs for electrical equipment. It introduces two machine learning predictive indicators: the Chromatographic Assay Indicator (CAI) and the Electrical Failure Risk Indicator (EFRI), which leverage chromatographic and sensor data, respectively. The CAI indicator was trained on external data, and its generalization capability was assessed using company-specific chromatographic data with expert assistance. The evaluation involved two heuristics, both focusing on keyword identification in free-text diagnoses. Results showed over 90% accuracy in predicting failures in reactors and power transformers, and an AUC of nearly 85% for reactors. Additionally, CAI outperformed classical Dissolved Gas Analysis (DGA) methods. On the other hand, the EFRI indicator was trained using company’s internal data from a monitoring system of operational sensors and maintaining power transformer data. The classification model achieved over 95% accuracy and an AUC of almost 90% on the test set. These results indicate that both indicators can be integrated into a solution to support maintenance specialists in their decision-making processes.

Exploring financial graph literacy: determinants and influence on financial behavior

Purpose Graphs are widely used in the banking and finance domain to support consumers’ decision-making process, but subjects differ in their ability to understand them. This study aims to detect the determinants of the ability to read and process financial information conveyed in the graphical format, i.e. financial graph literacy (FGL) and the relationship between FGL and subjects’ actual financial behavior (FB). Design/methodology/approach Data are collected by administering a structured questionnaire to the Italian adult population (n = 502). The survey includes different sections aimed at collecting information about sociodemographic and socioeconomic variables, financial literacy and FB. The econometric analyses are developed using OLS and Poisson regressions. Findings The results show that gender, geographical area, education, marital status and income are crucial determinants of FGL. Moreover, the analysis reveals that an increase in the FGL indicator is associated with a higher propensity for individuals to purchase banking or financial products or actively manage financial resources; results are robust, even controlling for financial knowledge. Originality/value Although previous research investigates the impact of graphs in financial decision-making, no studies measure the ability of consumers to read and interpret financial information conveyed in the graphical format. This study is the first to investigate the determinants of FGL and link it to actual FB. Implications for policymakers, regulatory and supervisory authorities and financial intermediaries are discussed.

Conceptual framework for enhancing decision-making in higher education through data-driven governance

In an increasingly complex and competitive landscape, higher education institutions must leverage data-driven governance to enhance decision-making processes. This paper presents a conceptual framework aimed at integrating data analytics into the governance structures of higher education, facilitating informed decision-making, and improving institutional effectiveness. The framework emphasizes the importance of collecting, analyzing, and utilizing data across various dimensions, including academic performance, student engagement, financial management, and operational efficiency. By adopting a data-driven approach, institutions can identify trends, forecast outcomes, and make strategic decisions that align with their mission and goals. The study outlines key components of the framework, including data governance, analytics capabilities, stakeholder engagement, and institutional culture. Data governance ensures that data quality, integrity, and security are maintained, while analytics capabilities empower decision-makers with actionable insights. Engaging stakeholders—faculty, administrators, students, and external partners—fosters a collaborative environment where data-driven decisions are embraced and supported. Furthermore, the framework addresses the challenges institutions face when implementing data-driven governance, such as resistance to change, data silos, and inadequate technological infrastructure. Strategies for overcoming these challenges include investing in professional development, enhancing data literacy, and promoting a culture of continuous improvement. The framework also highlights the significance of ethical considerations and transparency in data usage, ensuring that decision-making processes are equitable and accountable. The findings indicate that by embracing data-driven governance, higher education institutions can enhance their responsiveness to changing demands, optimize resource allocation, and improve overall institutional performance. Ultimately, the conceptual framework serves as a guide for higher education leaders seeking to harness the power of data in their decision-making processes, fostering a proactive and adaptive institutional environment.

Navigating ethical challenges of explainable ai in autonomous systems

The rapid integration of autonomous systems, such as vehicles, drones, and robots, into various sectors brings forth significant ethical challenges concerning their decision-making processes. This paper examines the role of Explainable AI (XAI) in addressing these challenges, particularly regarding accountability in the event of accidents and the necessity of human oversight in automated environments. We discuss the critical ethical implications of transparency, emphasizing how XAI can bridge the gap between complex algorithmic decision-making and public understanding, thereby fostering trust in these technologies. The paper also outlines the current regulatory frameworks for AI safety, analysing their effectiveness in promoting responsible innovation. Furthermore, we investigate the consequences of opaque algorithms, especially in life-critical applications where the stakes are exceptionally high. Through an analysis of case studies, we showcase how organizations have successfully implemented XAI to enhance safety measures and uphold ethical responsibility in their autonomous systems. Ultimately, this study advocates for the integration of XAI as a vital component in developing responsible autonomous technologies, ensuring accountability, and safeguarding public trust in an era increasingly defined by automation.

Impact of behavioral finance on financial decision making

This paper highlights the disproportionate effect of behavioral finance on investor’s investing decisions. While making any financial decisions, one easily gets influenced by psychological and emotional factors. Past individual experiences, tendency or opportunity for herd behavior, personal judgment, overconfidence in one's abilities, aversion to feelings of regret, reliance on advice from experts, and last but not least, something known as the disposition effect—by which investors tend to hold on to losing investments and professionally sell profitable ones too early. The impact of these factors on the investment decision-making process varies; on the one hand, they may enhance the quality of the decision-making process by aligning investments with risk tolerance and financial goals. However, on the other hand, behavioral factors can be a cause of inefficiency if investment decisions are based more on emotions than objective analysis. In our recent research, we questioned 187 investors, using a questionnaire from whom we acquired crucial information regarding the impact of behavioral finance on financial decisions. The primary objective of the questioning was to determine the effect of behavioral finance on financial decision-making. The secondary objective was to suggest some methods for making one aware of the repercussions of impulsive or emotional decision-making and making prudent investments.

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

Optimizing talent management in creative industries: Theoretical insights into effective database utilization

This review paper explores the optimization of talent management in creative industries through the utilization of advanced database systems. Traditional talent management methods often lack the structure and objectivity provided by modern database-driven approaches, which leverage comprehensive talent profiles, advanced search algorithms, and robust analytics to enhance decision-making processes. Case studies from industry leaders like Pixar, Wieden+Kennedy, and Ubisoft illustrate the practical benefits of these technologies in recruitment, development, and overall talent optimization. The paper also discusses challenges such as data quality, privacy concerns, and resistance to change, providing practical recommendations for overcoming these obstacles. Creative industries can significantly improve their talent management practices by fostering a culture of data-driven decision-making and investing in comprehensive talent databases. Future research directions include the impact of AI and machine learning, integration of emerging technologies, and longitudinal studies on the effectiveness of database-driven approaches.

AI and blockchain technology for secure and transparent financial transactions

The integration of Artificial Intelligence (AI) and blockchain technology presents a transformative approach to enhancing the security and transparency of financial transactions. Blockchain’s decentralized and immutable ledger system ensures that transaction records are tamper-proof and transparent, significantly reducing the risk of fraud and errors. AI, with its advanced data analytics and machine learning capabilities, further augments this ecosystem by providing real-time monitoring, predictive analytics, and automated decision-making processes. Together, these technologies enable more efficient and secure financial operations, from cross-border payments to trade finance and asset management. This paper explores the synergistic potential of AI and blockchain in revolutionizing the financial sector, highlighting key applications, benefits, and challenges. By leveraging the strengths of both technologies, financial institutions can achieve unprecedented levels of security, efficiency, and trust in their operations.

AI-driven robo-advisors on personal financial management and investment strategies

The advent of AI-driven robo-advisors has revolutionized personal financial management and investment strategies. Leveraging sophisticated algorithms and machine learning, these platforms offer personalized financial advice and automated portfolio management with minimal human intervention. By analyzing vast datasets, including market trends, economic indicators, and individual client preferences, robo-advisors can create tailored investment plans that optimize returns and manage risks effectively. This paper explores the transformative impact of AI in democratizing financial advice, enhancing decision-making processes, and making investment management accessible to a broader audience. The integration of AI in robo-advisory services not only improves efficiency but also provides a scalable solution to meet the diverse financial needs of individuals.

Public works, urban development, common goods

The role taken by public works requires us to use them to address the need to govern the complexity of the contemporary city, focusing its development and physical transformations on shared goals. The combination of urban regeneration and public works finds an outlet in intervention strategies for the quality of public space, public buildings, and architecture of civic value. In order to make public works an opportunity for shared regeneration, planning actions should aim at framing individual works within a broader strategic vision, dealing with the management of services (cultural, educational, social) to involve communities and stakeholders in organising urban enhancement methods. The construction of the ‘public city’ oriented to the common good should constitute a continuous path that allows neither pauses nor retreats. It should activate decision-making and governance processes that enhance citizens’ participation and develop synergies with private subjects, focusing on the social dynamics of the world of work, dwelling and culture.

Lessons learned from implementing a digital rehabilitation care planning platform to improve care access for patients with work disability: qualitative process evaluation of the RehaPro-SERVE study

BackgroundInpatient rehabilitation therapies can be applied for in Germany by patients of working age to support their return to work. However, there are some problems that impede an easy and uncomplicated application process. An interdisciplinary case management approach for rehabilitation care planning was developed to facilitate the access to rehabilitation. Case conferences (CCs) were held with relevant stakeholders and took place on a digital communication platform. We conducted a qualitative process evaluation to understand the implementation of the intervention and to identify contextual factors as well as mechanisms for a successful implementation in the context of primary care.MethodsThe process evaluation included interviews with primary care physicians (PCPs), patients and stakeholders involved in the intervention process. Reflexive thematic analysis was used to analyse the data. Emerging themes were structured according to the Donabedian framework of structure, process and outcomes.ResultsA total of 18 interviews were conducted. Important results included the desire for more patient involvement and case management. Patients especially valued the opportunity to receive support from a social worker. Limitations of the platform related to usability and limited opportunities for stakeholder communication. Despite training for PCPs, several problems arose regarding the clarity of the intervention process. Patients were satisfied with their application process and the treatments offered, while PCPs reported an increase in workload.ConclusionsA digitalisation of the application procedure for rehabilitation and further treatment options is acceptable to patients and personal support of a social worker is particularly valued. However, patients should be included in the CC in terms of a shared decision-making process. The digital platform requires sufficient training and adjustments have to be made to enhance usability and to improve the efficiency of the process for PCPs. Overall, the exchange between the various stakeholders in the CC is considered particularly useful in more complex cases.Trial registrationDRKS German Clinical Trials Register, DRKS0 00242 07. Registered on 22 March 2021.

Design and Implementation of a 7-Person Voting Device based on Quartus

As the complexity of digital system design continues to increase, efficient and precise decision support systems have become increasingly important. This paper presents the design and implementation of a 7-voter decision system based on Altera’s Quartus software. The voter is applied in the core decision-making process to ensure the accuracy and efficiency of decisions. The study begins by outlining the design principles of the 7-voter decision system, using a design philosophy that minimizes timing delays and optimizes hardware resource utilization. A complex majority voting logic circuit is implemented using high-level hardware description languages (VHDL/Verilog), ensuring the advanced nature and applicability of the voting circuit. In terms of the logical implementation of the voting circuit, the paper conducts an in-depth analysis of the optimization of combinational and sequential logic and standardizes the input and output to enhance the signal stability and the system’s resistance to interference. On the Quartus platform, the design process of the voting system is elaborated in detail, with careful consideration given to boundary conditions and hardware compatibility. The implementation of the voter has undergone rigorous simulation and testing. Strict parameter setting for simulations and waveform analysis confirm the correctness of the circuit, and fault simulation tests further confirm its reliability. Performance indicators such as response time and percentage of resource utilization have been quantitatively analyzed and meet the design objectives set beforehand. Overall, this research not only realizes a highperformance 7-voter decision system but also provides an effective case study for applications on the high-performance Quartus design platform. Future research can build on the current work to further optimize algorithms and hardware, explore the potential for the application of the voter in a broader range of fields, and adapt to the new features of the Quartus platform to achieve more powerful decision support functions.

Designing the public works values

What are public works, and under what conditions can they be considered a “common value” recognized by the community? The contributions to issue n. 28 of TECHNE Journal delve into these conditions, offering critical reflections and research advancements that examine cities, buildings, places, and public services from the perspective that «the entire urban space must be regarded as something produced, owned, and transformed collectively» (Marella, 2024). There are three main areas of reflection and proposal: the first involves the methods and procedural and design tools that can be employed for the recovery and adaptive reuse of public works facing functional obsolescence, abandonment, and/or decay; the second addresses the regenerative processes of public spaces and entire urban sectors with a focus on usability and environmental sustainability; and the third concerns the participatory design of public works as a vital lever for the creation of social value. This set of design actions is well-supported by analytical and evaluative tools that guide the decision-making process, all driven by an interest in optimizing the socio-economic, cultural, and environmental benefits that can result from more effective and efficient public works production and management processes, especially in response to evolving social demands.

Lacking Accountability and Effectiveness Measures: Exploring the Implementation of Mentoring Programs for Refugee Youth

A high proportion of refugees coming into the U.S. are under the age of 18, and many continue to struggle to attain basic educational qualifications which may lead to viable economic opportunity. Recently, the Office of Refugee Resettlement (ORR) began issuing youth mentoring (YM) grants to support the integration of refugee youth. This paper explores the decision-making processes of resettlement organizations in a vertically complex network of organizations which implemented the YM programs for refugee youth in one large city of Texas. Through a series of semi-structured interviews with program implementers, findings suggest that resettlement agencies remain powerless in making key programmatic decisions, including implementation of the programs and designing accountability and effectiveness measures, which can truly evaluate the program quality. Other findings suggest that across the network of organizations involved in implementation of the YM programs, compliance with federal regulation is prioritized over accountability. The lack of program effectiveness and accountability measures have implications for one of the most vulnerable population, refugee youth. The main recommendation for policy and practice argues for the lead public agency’s increased involvement in defining key outcomes for the program recipients in a vertically complex network.

A Critical Study of Psychology of Retail Investors

This research critically examines the psychology of retail investors, focusing on the behavioural finance approach to investment decision-making. Behavioral finance challenges the traditional assumption that investors are always rational, proposing instead that psychological, social, and cultural factors influence financial behaviours. The study aims to explore how demographic factors such as gender, age, and investment experience, along with macroeconomic conditions like interest rates and inflation, shape retail investors' decision-making processes. By analysing both quantitative and qualitative data from surveys and questionnaires, the study seeks to uncover patterns in how emotions, cognitive biases, and social networks affect investment choices across different groups. The scope of this research extends to identifying significant variations in investment behaviour based on demographics and cultural influences. Moreover, the study will test hypotheses related to gender differences in the influence of peers and interest rate changes on investment decisions. Through a mixed-methods approach, this research aspires to provide valuable insights for financial institutions and individual investors, enhancing their understanding of the psychological drivers behind market behaviour and investment strategies.

Revealed and stated preferences for sharing of patient-reported data in Austria

Abstract Objectives Routine collection of health data is essential for creating sustainable, efficient, and equitable health systems. While administrative health data are collected automatically, successful routine collection of patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) depends on voluntary participation. The aim of this study is to investigate how to increase participation in routine patient-reported data collection. Methods This mixed methods study used representative survey data from the Austrian population aged 18-70 (N = 1260) and 15 semi-structured narrative interviews with citizens and health data experts collected in 2024. Survey respondents were shown examples of PREMs and PROMs and indicated their willingness to share them, depending on the recipient and purpose. Interviewees answered the same questions while providing insight into their decision-making process, which was analyzed using thematic analysis. Wilcoxon rank sum tests were used to assess differences in willingness to share across recipients and data collection purposes. Results Survey respondents were more willing to share PREMs than PROMs (“rather yes” vs. “rather no”, p < 0.001). One explanation given in the interviews is that PROMs are more personal and convey sensitive information. In terms of sharing PROMs, people were most likely to share with their GP (81%) and least likely to share with private companies (7%). Facilitators for sharing identified in the interviews were improving personal care and the health system, while barriers were use for profit generation and fear of negative consequences for the person sharing. Conclusions The results suggest handling the collection of PROMs demands greater caution compared to PREMs. Furthermore, clearly communicating personal and societal benefits arising from the collection and analysis of self-reported health data may increase participation in routine self-reported health data collection. Key messages • The collection of self-reported health data depends on voluntary participation. Clearly communicating personal and societal benefits of sharing self-reported data can motivate participation. • Self-reported data collection is essential for sustainable and efficient health systems. Compared to PREMs, people are more reluctant to share PROMs because they contain sensitive information.

Stratification risk computation of primary graft gysfunction in heart transplant recipients on a smartphone application

Abstract Introduction There are no standardized guidelines for helping graft allocation in heart transplant (HTx) and accepting transplants from marginal donors and donors after circulatory arrest make decision making even more complex and time consuming than ever. The organ allocation process should include a risk analysis for primary graft dysfunction (PGD) that represents the most dreadful complication of HTx. There are 3 score systems validated to predict PGD but none has clear superior predictive capability compared to the others and the complexity of score computation potentially discourage their clinical use. A score integrating all the variables presented in a user-friendly format should improve efficacy and safeness of the organ allocation process. Methods Severe PGD defined as need for temporary mechanical circulatory support (ECMO) in the first 24 hours following HTx was considered the end-point for risk stratification computation. As a derivation cohort we used meta-analytic approach for identifying variables that are independently associated to severe PGD. The overall prevalence of each variable was determined to define the absolute risk increase value in presence of each factor. We then created a scoring system applying multivariate logistic regression model. As a validation cohort we studied the performance of the score in a retospective series of HTx performed at our institution from 2013 to 2022. We used C statistic equivalent to the area under a receiver-operating characteristic curve for dichotomous outcomes. Values above 0.7 were considered acceptable. We eventually developed an application for smartphone for risk computation. Results On 64 papers screened, 5 studies were considered eligible for the derivation cohort definition. Cumulative number of patients considered was 10’175 with an incidence of severe PGD of 7.1% (CI 5.3 - 10.4) and a 30-day mortality of 41%. 13 variables were identified (table 1). The ROC curve predicted the use of ECMO with the specificity and the sensitivity of 0,81 and 0,92 respectively. We have included in the validation cohort 128 out of 150 transplanted patients, excluding pediatric and double organs transplants. The mean donor age was 45 +/- 12,4 years, 72 (54%) were men. The mean recipient age was 57 +/- 9,3; 92 were men; 32 recipients (25%) needed ECMO support within 24 hours post-transplant; 75% of the donors in the ECMO group were > 40 y.o.; 30 days mortality was 3.1% and 6,2% in the non-ECMO and ECMO group respectively. We validated the model on our cohort defying a binary outcome (ECMO yes/no). Conclusion Clinicians fill donor, recipient and surgical parameters in user-friendly smartphone application identifying patients that are most likely to need ECMO support post HTx. The decision-making process during organ allocation evolves from what was once little more than personal experience and intuition to a reliable evidence-based method based on personalized risk prediction.Table 1

Decision-making under pressure: ethical considerations in COVID-19 policy making in Switzerland

Abstract Background Policy making and science were deeply intertwined during the COVID-19 pandemic, particularly in justifying health policy measures with ethical considerations being at the core of decision-making trade-offs. However, not much is known about the actual ethical challenges encountered by policy makers and scientists involved in policy advice. This study aims to explore the ethical challenges in COVID-19-related political decision-making as perceived by Swiss policy makers and scientists involved in policy making, the role of ethics advice, and what can be learned for future public health crises. Methods Thirteen qualitative expert interviews were conducted with policy makers and scientists involved in COVID-19 policy decisions in Switzerland at regional and national levels. We used inductive thematic content analysis to analyse the interviews. Results Key ethical challenges included making trade-offs between common vs. individual good and economic welfare vs. health of the population, proportionality of policy measures, and public capacity to handle uncertainty. Opinions varied on the sufficiency of ethical considerations on the policy level. Constraints included a lack of time in the fast-paced dynamic of the pandemic, ethics as a complex subject area, its overlap with law, too much focus on few topics such as vaccination-related questions, and individual character traits, such as the dominance of medical professionals over ethicists. While ethics support was deemed adequately present in the decision-making process, there were calls for more ethics training, involvement of the public in the discourse, and for accompanying communication to build trust among the population for the future. Conclusions The study offers empirical insights into the ethical considerations of COVID-19 policy making in practice in Switzerland, providing a basis to enhance ethics support for future crises also in other countries. Key messages • This study uncovers the key ethical considerations in Swiss COVID-19 policy making, emphasizing constraints to sufficient ethical discussions. • Enhanced ethics training and greater public engagement is needed in future health crises to build trust and improve decision-making in future health crises.

Development and implementation of a guideline-based decision support system for etiological diagnosis of Sustained Monomorphic Ventricular Tachycardia (SMVT)

Abstract Background Guideline (GL)-based decision support systems (DSSs) play a significant role in supporting physicians in their decision-making processes by providing structured clinical guidance. While these systems have shown significant success in enhancing practitioner performance and patient outcomes in general practice, their application in the context of ventricular arrhythmias remains limited. Moreover, the clinical etiological diagnosis of ventricular arrhythmias presents various challenges for healthcare professionals, such as adherence to updated diagnostic criteria and understanding of complex arrhythmia mechanisms. Purpose In this study, we aim to design, implement, and evaluate a guideline-based decision support systems for etiological diagnosis of sustained monomorphic ventricular tachycardia (SMVT), which could potentially address critical challenges associated with the disease diagnosis and management. Methods To comprehensively capture the semantics embedded in clinical guidelines, we first developed a dedicated knowledge graph tailored to the clinical decision-making. This involved extracting entities and relationships pertinent to key decision procedures from ESC guidelines for ventricular arrhythmias, clinical textbooks or literatures. Subsequently, we translated instances within the knowledge graph into archetypes within openEHR, amplifying the interoperability of clinical information. To enhance the interpretability of the DSS, we established executable diagnostic and treatment processes to offer visual representation of clinical steps in diagnosis and treatment. Ultimately, we constructed a decision support engine to implement the GL-DSS, and its effectiveness was evaluated with anonymous real-world data. Results The proposed GL-DSS was found to be quite feasible and could provide etiological diagnosis for SMVT. The knowledge base for the constructed GL-DDS included more than 2000 entities and relations, which played a vital role in representing disease pathogenesis. The proposed system can map the clinical entities to a set of openEHR archetypes, thus mitigate the interoperability issues of the hospital information systems and CDSS data integration. Also, this module improved system usability and ensured that guidelines could be easily translated and updated into decision support tools. Real-word medical records from 20 patient were collected for model evaluation, the results showed that the GL-DSS covered most of the entities and relations between diseases and diagnostic evidence recorded in clinical texts and achieved a diagnosis accuracy of 80%. Conclusions The results demonstrated that our proposed GL-DSS could completely and accurately capture the diagnostic information under the concrete EHR data. Thus, the implement decision support system could have the potential to improve clinical decision-making, patient care, and outcomes in this specialized medical domain.