European Journal of Advances in Artificial Intelligence
The European Journal of Advances in Artificial Intelligence (EJAAI) — ISSN (Online): 3050-9564 — is an international, open-access, peer-reviewed journal published quarterly by ESA Research. Indexed in Scopus (Elsevier), DOAJ, CrossRef, and Google Scholar, EJAAI publishes original research across all areas of Artificial Intelligence including Machine Learning, Deep Learning, NLP, Computer Vision, Robotics, Generative AI, Explainable AI, and Applied AI. All articles are published under CC BY 4.0 with immediate free access worldwide. EJAAI operates double-blind peer review and complies fully with COPE and DOAJ ethical publishing standards.
AI-Augmented Pipelines for Cloud-Native Fintech Security
Generative AI has found its way into many parts of IT operations and delivery, including DevOps. Despite the capability of Generative AI to serve as a virtual chatOps companion for the DevOps teams, enabling and improving AI capabilities in DevOps remain a nebulous concept. Secure and scalable cloud-native financial systems require robust preventive and detective controls that are part of the security, compliance, and risk management processes. Although many existing works highlight the advantages and benefits of AI-enabled cloud-native DevOps pipelines, their findings still remain disconnected from a holistic Generative AI-enabled DevOps solution for cloud-native financial platforms. The following sections propose such a Generative AI-enabled DevOps model where Generalized Large Language Models (GLLMs) are integrated into different stages of the DevOps pipeline. A non-exhaustive representation of the enhanced DevOps stages is shown. The AI-enabled DevOps pipelines are evaluated on multiple parameters, and the results show significant enhancement across security, reliability, or compliance in the cloud-native financial platform. Furthermore, the AI-enabled DevOps pipelines are evaluated on establishing resilient services without downtime and supporting the required scaled-up or scaled-down throughput. The evaluation results substantiate the importance and positive impact of embedding Generative AI capabilities in further enhancing control and compliance of the financial service.
Copyright (c) 2024 European Journal of Advances in Artificial Intelligence
Deep Learning for Secure Cloud-Native Financial Transactions
Cloud-native transaction-processing architectures for the global financial infrastructure must be genuinely secured by design. Failure to achieve this exposes all participating financial institutions—those issuing transactions and those executing them—to the systematic risk of insolvency as well as ruinous loss of public and institutional confidence in the cloud-native financial platform. Challenges abound: existing cloud infrastructure was not architected to meet the requirements of a global financial market. Recent advances in product and fraud identification using deep learning (DL) highlight an important opportunity for securing transaction processing serving the entire banking community and all financial markets. These motivating factors confirm the need for research into identifying and mitigating the practical risk of secure transaction processing in cloud-native bank-and-trust-worthy network transaction-request processing of central bank digital currency retail payments of the wholesale banking industry’s customer base of the population at large. Pragmatic study into the use of DL for the identification of products, merchants, and service suppliers involved in genuine transactions—and for ongoing fraud detection in transactional ecosystems—can mitigate the risk of systematic fraud exposure in cloud-native transaction-processing infrastructures serving the global financial system. The research also highlights the risk and cost associated with alternative self-built, bank-specific approaches to product, merchant, and supplier fraud identification—an aspect that is frequently overlooked.
Smart Agents for Risk and Fraud Intelligence in Insurance
A concise objective, evidence-based abstract for a study of agentic AI-powered risk prediction and fraud detection for insurance data platforms. By facilitating shared decision-making by underwriters and data scientists, agents, and risk, fraud, and customer experience teams, it is expected that agentic AI will improve predictive performances, generate richer abnormality detection signals, and outperform traditional AI as a risk and fraud detection tool. Empirical results from an insurance portfolio sample support this hypothesis, demonstrating agentic AI predictive accuracy, good fraudulent event discrimination, and sensitivity and specificity scores exceeding existing solutions. Although detecting fraud remains challenging, agentic AI ranks customers by the likely probability of fraud occurring in the next month, supporting team priorities. The functioning and convergence of different teams, systems, and data within insurers are essential foundations to improve alerts received and minimize impact on customer experience. Agentic AI diverges strategically and functionally from traditional AI, which is based on automating decisions and minimizing human intervention. Agency incorporates the latest AI methodologies, progressing a step further by aligning data science and business teams hierarchically. Agentic AI performs as well or better than non-agentic when responding to the same alert signals, enabling detection of potential future frauds while refining customer experience. Future research prompts should explore governance aspects and the value of agents for companies.
Federated AI for Cross-Enterprise Risk Intelligence in the Cloud
To facilitate federated AI and federated cloud architectures for cross-enterprise Risk Intelligence in 2025, the study synthesizes its aims and scope, establishing relevance and impact. Cross-border Risk Intelligence engages insights from distinct Economies for local Governance use. Analysis emissions Cross-Border Information Act compliance, especially at non-country cartel jurisdiction breaks. Cross-Economy Data Management Agency amid disparate Risk Assurance requirements, perpetual audit, activity-based Cross-Economy Digital Services Levy, and unalignment with foreign standards risk disintegration of worldwide trade. Distorted governance dash move to afford globalisation shortcuts via far-from-market subvention need Countervailing Duties—economically falling prove hards through regulatory action, indeed guising phase modulation . Cross-enterprise Risk Analytics thus examine Capital-Source Reporting Standard Data Residency. Preventing dissatisfaction then demand Cross-Border-Compliance-Are Insistence—protocol shopping demand is collapse of a tough wall before regulator. Federated Cloud Multi Cloud and Hybrid Cloud Platform-Are Insight Demand. Latency-Sensitive Federation-Proximity Need Advance Deployment of Cloud-Capacity Engine for Real.Time Data Pre-Processing-Streaming-Risk Signal-Online-Offline Fusion. Domain-/Risk-Signature Stemming Edge-Cloud Edge for Excess-Rate-Deplorable Distance Latency-Sensitive Applications Finally-Are Imperatives Hence-Force Federated Cloud—Federal Cloud—Federated Infrastructure-Are Interest Demand in All.
AI and Big Data for Cross-Border Financial Compliance
2025 promises to become an inflection point in regulatory compliance monitoring. AI technologies, both application and feature engineering techniques, such as Large Language (LLM) and Generative AI, are currently receiving an unprecedented level of attention from regulators worldwide. These technologies now face the imminent prospect of deployment regulation globally. AI solutions are expected to impact nearly every domain of economic activity. Yet, the proportion of AI systems subjected to regulatory oversight remains small. Fashioning a balanced regulatory regime that ensures that the risks of AI systems are recognized and managed is a pressing coordination task. Growing off-the-shelf modular capabilities will necessitate a focus on AI development, deployment, and use by private sector actors beyond the large enterprises. The research conducted by Niyato et al. is informed by the view that, if successful, these measures, together with focused support for the development of AI technologies that address national priorities and international obligations, will enable Canada to seize the opportunities presented by AI while also mitigating the risks. AI regulatory compliance represents a step toward actual monitoring of AI systems to ensure that those systems – and the data fed into those systems – are compliant with expectations and controls stipulated by regulators across the globe in their respective AI regulatory frameworks. Such monitoring should help maintain both the regulated domains of society and the integrity of the operated AI systems. AI technologies have been used extensively to automate software testing for evaluation. Organizations building such monitoring capabilities can be expected to operate the function of regulatory compliance as a managed service, with the potential to offer such services as an end product to other organizations.
Copyright (c) 2025 European Journal of Advances in Artificial Intelligence
Legacy Decoupling Patterns for Health Insurance
Monolith coupling in health insurance platforms impedes growth as business environments change. The Migration Strangler Pattern offers a decoupling strategy, but selection of additional patterns lacks empirical support. Qualitative architectural analysis identifies and describes a catalogue of 14 patterns that address remaining decoupling concerns across business areas such as observability and monitoring, deployment and operations, risk management, and team organization. A synthesis of nine architectural principles supports the patterns. Evidence from three health insurance case studies assesses pattern applicability, clarifies trade-offs, and expresses cost-benefit considerations. Introduction Monolith coupling in health insurance platforms limits scalable growth in increasingly volatile business environments. The migration-supporting Migration Strangler Pattern offers a single decoupling pathway, but additional patterns for related concerns across operational domains remain poorly substantiated. A qualitative architectural analysis identifies and describes a pattern catalogue covering 14 additional transformation avenues associated with observability, deployment and operations, CI/CD, security and compliance, and team organization. The catalogue is framed by a synthesis of nine architectural principles for supporting decoupling outside direct functionality. Patterns are grounded in practical experience from three health-insurance-sector case studies that attest applicability, clarify trade-offs, and express cost-benefit considerations.
Smart Default Prediction A Machine Learning Approach
The growing importance of machine learning in decision-support systems has spurred substantial research into applying machine learning models to data from multiple sectors. Available open-source data on loan defaults has been used to train a variety of machine learning models from statistical models to complex neural networks. The performance of the models is evaluated using various information-theoretic and statistical measures. In addition to a detailed evaluation of the models, several elements associated with their practical adoption in a bank have been presented. Machine learning algorithms are now widely used and external advances in these methods have come, in large part, from academic research. That said, the focus for these models is now shifting to practical elements such as deployment, interpretability, regulatory compliance and operational aspects. To this end, data publicly available from the Lending Club, a leading US provider of loans to individuals and small businesses, has been used to develop different machine learning algorithms for prediction of the default of loans. The deployed solution uses tree-based methods both for interpretability as well as compliance with regulations.
Copyright (c) 2026 European Journal of Advances in Artificial Intelligence
Agentic AI for Autonomous Compliance in Data Centers
Enterprise data centers are tasked with managing large-scale applications and data for multiple businesses, making them private distributed systems in the cloud computing paradigm. Data center operations are inherently risk-prone, and commissions and omissions often expose the data center and hosting customers to record fines for non-compliance with sector-specific security, privacy, and operational continuity standards. Failing to detect and fix issues in time can also lead to operational infractions that directly affect business continuity, security breaches, data leaks, and a multitude of other service disruptions. Therefore, risk detection and compliance remediation are missions conducted by specialized teams, following formal processes and often using dedicated tools. Although risk detection and compliance remediation are traditionally viewed as human-centric, autonomous solutions exist and bring associated advantages. Nevertheless, they rarely address standalone data center needs, either operating as unsupported external tools (e.g., vulnerability scanners) or targeting only some types of issues (e.g., anomaly detection, fault diagnosis, etc.). A new class of general-purpose, self-driving data center risk detectors conforms to a sensing-perception-reasoning-design-execution action and feedback loop and, endowed with proper control mechanisms, can automatically contain, remediate, and restore. Such detectors generally require either ad-hoc policy engines for compliance mandates or proactive prevention workflows. In agentic AI frameworks, supervisory activities such as auditing, verifying, and explaining detected incidents also benefit from automation.
Copyright (c) 2023 European Journal of Advances in Artificial Intelligence
Continuously Learning Healthcare Systems
The pursuit of medicine-informed artificial intelligence (AI) is undermined by current methods lacking the capacity for continual adaptation to changing operational contexts. Such self-evolving intelligence is crucial in rapidly changing domains like healthcare. AI systems accessing real-time clinical pipelines can dynamically modify their knowledge base or data processing, incorporating newly available data categories, features, relations, or concepts. A self-evolutionary architecture incorporating AI in medicine-informed continual learning is outlined, with supporting infrastructures for data quality, data governance, privacy risk, and benchmarking. Completed short-term research constitutes a first instantiation for real-time risk stratification of patients in acute medical care with guaranteed performance. These developments contribute to the establishment of healthcare AI as continuously self-adapting, incrementally sound, and clinically reliable. AI applied in healthcare consistently presents with an established and salient conceptual gap, despite ambitious and sophisticated application, deployment, and development efforts. Most operational AI models in healthcare are not continuously self-evolving. Thus they invariably become increasingly misaligned with information-rich, time-variant clinical environments and operations, gradually losing medical relevance and becoming actively misleading—a risk given the considerable and growing influence of such inferences on patient care—under a quasi-Boydian–Hyesque risk governance concept. Assurance of continually adaptive AI in both architecture and clinical performance necessitates an evolutionary impulse for AI.
Engineering Scalable AI Analytics for Electronic Health Records
Research aims, methods, key results, and implications for scalable EHR analytics and precision care. Electronic Health Records (EHRs) are a historical, comprehensive, and continually updated source of health data for patients. Although EHRs have been widely used for retrospective population studies, research prototypes, and clinical decision support systems, the actual uptake of AI-driven technologies in clinical practice and patient care remains limited. Automatic ingestion of clinical data, standardized data lakes, and periodic model retraining are essential for seamless operation and scalable usage. The objectives of this research are to design data ingestion and preprocessing pipelines that run automatically, to support cloud- and on-premises-based clinical analytics, and to apply patient stratification and personalized treatment optimization methods. The proposed technologies are scalable, reproducible, and support precision healthcare delivery, with a global public health perspective. The results of the clinical data engineering effort are publicly available. Reproducibility was achieved by adhering to open-source principles, data-sharing best practices, and the use of common data-collection and storage formats. Population analyses demonstrated predefined hypotheses in the domain of obesity and revealed new insights into atrial fibrillation. In acute care and emergency medicine, AI-supported rapid data ingestion and the clinical application of complex models during patient triage were successfully demonstrated. Although deployment-specific bias was identified in these specific domains, adequate technical control of both cloud-based and on-premises configurations was supported.
An Event-Driven Architecture for Real-Time Financial Crime Intelligence
Financial crime detection and prevention remain a challenging yet vital element in the fight against criminal activity. Increasing volumes of transactions and major technological innovations such as cloud computing, big data, the Internet of Things (IoT), artificial intelligence, and blockchain have led to the theoretical exploration of event-driven regulatory technology (RegTech) – a new regulatory-compliance paradigm that focuses on dynamic regulatory monitoring of complex event-processing systems in various domains. Built upon the concept of event-driven RegTech, FinCompliance 2.0 represents an intelligent compliance architecture that is designed to facilitate the automatic detection of financial crimes with minimal human involvement. The architecture is constructed as a combination of an event processing layer, flexible compliance rules, and intelligent detection mechanisms. The effectiveness of the intelligent detection methods is evaluated using several benchmark datasets from the financial domain, and results reveal that their performance is on par with state-of-the-art algorithms. The growing trend of processing massive amounts of transactional data via complex event-processing (CEP) systems has not only increased the volume of data streams and elevated the incidence of financial crimes, but has also created new business opportunities for malicious actors. Surveillance and preventive measures are often implemented around static rules; however, financial crimes can persist undetected for long periods precisely because of obliviousness to the absence of detectors for known patterns or the disregard of evolving knowledge, which includes changing actors and behaviours, evolving landscapes and challenges, and so on. In recent years, new advances in cloud computing, big data, and IoT technologies have led to the exploration of event-driven monetary supervision management and event-driven RegTech, on the basis of event-related concepts for regulatory compliance and acting monitoring.
Happiness is a multidimensional, subjective concept reflecting individual and collective well-being and quality of life. The multidimensional scaling of happiness using a survey of 734 ward-level residents and geospatial methods of analysis revealed patterns and relationships between happiness, urban form, and infrastructure. Happiness mirrors patterns of public amenities and services such as health and education. A generalised linear model indicates that happiness is positively associated with density, transport access, and greater green space area and negatively related to housing quality. Integrating happiness considerations into planning enhances urban design and service delivery, thereby facilitating better quality of life in rapidly growing cities like Muzaffarpur. In the absence of time series, seasonality, and high-resolution data sources on physical form and utilities, the analysis is exploratory. The study offers an objective, evidence-based analysis of happiness in Muzaffarpur City using a spatial framework. This approach aids in evaluating the relationship between happiness, urban structure, and infrastructure and provides valuable insights for future urban planning and resource allocation. The study contributes to ongoing discourse on urban form–happiness relationships and demonstrates the integration of the spatial dimension of happiness into geospatial analysis.
Copyright (c) 2026 European Journal of Analytics and Artificial Intelligence (EJAAI)
Smart Compliance AI Auditing for Green Manufacturing
The degradation of the environment, welfare inequalities, and water scarcity are forcing organizations to consider their implications for the economy, society, and governance. However, organizations increasingly struggle to cope with these sustainability aspects and their interrelatedness. Both have implications for compliance with a growing number of sustainability standards. An AI-empowered compliance audit framework for assessing sustainability in manufacturing infrastructure is proposed. The technological framework aims to implement the audit in a fully automated way by integrating audit-related data from manufacturers and simplifying the audit process. It is based on proprietary audit algorithms that rely on machine learning, natural language processing, and reasoning. Due to the digital audit provision and knowledge management capabilities, the framework highlights the need for proper governance, data integration, and aligning the interests of credit rating agencies, manufacturers, and the ecosystem. Research structures are naturally susceptible to changes brought about by environmental and technological shifts. The pandemic illustrated how fragile the social structure is and how technology can help build resilience. It is widely acknowledged that improving environmental conditions, increasing social welfare, and developing adequate governance models are essential for sustainable long-term economic development. However, the interaction between these three aspects remains unclear. Balancing this relationship has become even more difficult for manufacturing organizations relying on natural resources. Water scarcity not only threatens the quality and quantity of essential goods but also imposes greater restrictions on the proper functioning of production plants, infrastructure, and employee well-being. Therefore, a particular focus on sustainable manufacturing infrastructure is crucial.
Copyright (c) 2025 European Journal of Analytics and Artificial Intelligence (EJAAI)
EHR-Based Deep Learning for Clinical Prediction
Deep learning techniques surprise experts and enthusiasts alike with constantly improving performance in an increasing number of application areas, such as image classification, natural language processing, and speech recognition. Early adopters observe tangible competitive advantages and are reaping the benefits of deploying deep learning systems in production. Healthcare has great potential for improving predictive analytics with rich data from electronic health records. Thanks to the extraordinary capacities of deep neural networks, large volumes of heterogeneous, multi-structured EHR data can be analyzed more accurately, leading to higher quality healthcare outcomes. Anticipating patient deterioration, identifying high-risk individuals, and predicting the development of specific diseases disenable timely and targeted interventions. Despite the abundance of data, the clinical insights of risk profiles and predictive models have not yet materialized—major hurdles include high maintenance costs and associated resource requirements. Innovators must customize and deploy new systems with every project, and embedding predictive models into clinical decision support systems remains a dream rather than reality. The rich variety of EHR data is usually processed in the service of a specific task rather than for broader, production-scale deployments, limiting investment returns. First-serving deep learning models demonstrate evidence of predictive capacity, but validation, implementation, and integration across EHR data modalities often receive little attention
Predicting Sustainable Concrete Performance Using AI
Performance prediction of concrete exhibiting extreme average performance, or a sufficiently accurate upper bound on performance, can be achieved with a comparatively small number of high-performance samples. AI-assisted performance prediction thus offers an attractive research avenue for use, for instance, in accelerating the quest for ideal material compositions allowing for reduced emissions while fulfilling performance criteria. When trained on a suitable dataset, phonetic characteristics from AI-driven models can demonstrate how changing a specific variable influences performance outcome, thus providing insight for design. Moreover, when calibrated with the appropriate input parameters, AI-based prediction models can be incorporated into broader Civil Engineering models to account for durability of surface materials used in infrastructures such as bridges or tunnels exposed to specific environmental loading. The AI approach can be used advantageously when applied in areas where specialised knowledge is limited or when it is difficult to explore a problem space adequately via simulations. Results presented in this study therefore aim to advance these areas with consideration to sustainability by demonstrating a specific AI model capable of increasing the development speed and enhancing the knowledge regarding a considered concrete. Further results will support the analysis of durability of campuses and Civil Engineering assets under specific environmental loading and will pave the way towards a PhD research topic encompassing planning and support for future interventions in Rock Engineering related scenarios.
Copyright (c) 2024 European Journal of Analytics and Artificial Intelligence (EJAAI)
Unsupervised FHIR Stream Anomaly Detection
Reports of serious security breaches involving medical data have attracted increasing public attention. These incidents emphasize the importance of protecting health information and re-establishing patient trust in information systems that support healthcare delivery. Guaranteeing the confidentiality and integrity of healthcare data is challenging, given the intrinsic requirements dictated by openness, interoperability, accessibility, and availability. In addition to the overall emergence of disorder, active, unattended FHIR data streams produced by thousands of devices, systems, and services are particularly prone to contain high-level attributes and momentary deviations that fall outside accepted monitoring thresholds or general patterns. Such anomalies may indicate dangerous problems, progress toward disastrous events, or even the introduction of malware. However, it is hardly possible to use supervised techniques to define the rich set of expected patterns that characterize or govern real-time healthcare data streams. Unsupervised learning methods are presented with an objective, evidence-based analysis and formal structure. Emphasis is placed on the application of these approaches to real-time anomaly detection within unattended and open healthcare data streams that follow the Fast Healthcare Interoperability Resources (FHIR) standard. The analysis offers a heuristic indication of the methods that can best exploit the availability and characteristics of real-time healthcare data streams. The methods are systematically examined against qualifying criteria and success factors for their application in real-time healthcare environments
RL-Based Adaptive Control for Autonomous Vehicles
Adaptive control of autonomous vehicles in dynamic mixed-traffic environments has emerged as a trendy research direction. Most studies achieve adaptive control through planning, control, or generalization. Unlike planning-based methods, control methods rely on a predefined driving policy, which limits adaptation capability. Moreover, the decision horizon remains static and is not related to the surrounding driving conditions. Therefore, training of target driving policies based on previous traffic samples for similar traffic conditions is essential for potential improvement. Reinforcement learning is one of the most applicable techniques to solve such tasks. The development of model-based reinforcement learning requires a simulator. High-fidelity driving simulators, such as SUMMIT and CARLA, exhibit unknown dynamic properties, and thus they are mainly used to develop and test planning-based algorithms. They are not appropriate training simulators because trained networks do not perform well in the real world. However, perception and planning-based modules of autonomous driving systems are usually implemented using symbolic or engineering solutions. Based on such symbolic planners, sampling-based planners, or pre-planned trajectories, a driving-policy learning framework is formulated and a driving policy is learned in the context of supervised learning. This driving policy can serve as a training vehicle for potential future driving-policy adaptation in real vehicles using reinforcement learning. In addition, the proposed learning framework enables other adaptive control methods, such as reinforcement-learning-based methods.
Scalable Data Pipelines for Multi-Hospital Networks
Healthcare is increasingly recognized as a data-intensive industry. Multi-hospital networks, among other organizations, face mounting operational and governance challenges because of rigid data-integration pipelines that support all data sources and destinations in the network. These pipelines have become difficult to modify, causing them to lag behind the changing needs of the clinical operation. Scalable data-pipeline architectures better support clinical decision making, optimize hospital operations, ease data quality and compliance concerns, and contribute to improved patient outcomes. Meeting scalability goals requires breaking up monolithic data-integration pipelines into smaller decoupled components and aligning service-level agreements of pipeline components and source systems. Parallelization and adoption of distributed data-warehouse technology mitigate the burden of ingesting data into a multi-hospital network. However, latency requirements still warrant the construction of separate pipelines for data ingress from clinical devices, electronic health records, and external laboratory-information systems. Healthcare associations recommend near real-time data availability for a growing list of clinical and operational applications. Mishandling the real-time ingestion of data from clinical devices, in particular, compromises availability and performance. Scalable architectural patterns for real-time streaming Ingestion from heterogeneous data sources, transport processes, and back-end processing structures are detailed.
ML-Driven Clinical Insights for Health Equity
Healthcare predictive analytics draws on advanced machine learning techniques to identify the risks, needs, and likely outcomes of patients in large clinical populations. Such predictive models can enable timely preventive and therapeutic interventions tailored to the individual patient and leapfrog disparate health services towards more equitable delivery. Enabling healthcare predictive analytics involves addressing a number of underlying requirements. First, predictive analytics call for large, representative clinical datasets that capture the information required for prediction, including data on the eventual outcome and associated risk factors. The most common sources of such data are electronic health records, routine administrative datasets, medical imaging, genomic, transcriptomic, and other omics data; such data sources can be prospectively integrated and linked across the life course of a patient to support prediction with longitudinal models. Second, predictive analytics are effective for addressing prediction tasks for which sufficient data are available. Such prediction tasks are usually framed as supervised machine learning problems, in which a model is learned from a training dataset, subsequently evaluated on a held-out test set, and finally deployed in the clinic. Unsupervised and semi-supervised approaches can also support predictive healthcare analytics by discovering subgroups of patients that are at elevated risk of an undesirable outcome. Third, prediction is usually only the first step; time-series or sequential models can leverage the results of prediction to support healthcare decision making, guiding timely allocation of resources to discrete cohorts of patients with specific needs.
Large Language Models for Clinical Knowledge Extraction
Large Language Models (LLMs) can facilitate structured knowledge extraction from unstructured clinical information sources, such as electronic health records (EHRs), clinical text notes, and clinical trial protocols. This capability bridging heterogeneous data modalities has the potential to power a broad range of downstream clinical decision support and data mining tasks. Clinical informatics research often relies on specific types of information that are not readily available in explicitly structured form in the corresponding data repositories. These “structured knowledge” types are prevalent in a variety of clinical data sources, such as coded EHRs, but LLMs can offer a different, complementary approach. The task of mapping raw unlabeled text to structured knowledge types involves providing rich supervision during LLM training—using either fine-tuning or prompting mechanisms—to create and support structured knowledge extraction capabilities.
Forecasting Readmission Risk Using Structured Clinical Data
Increasing emphasis on reducing hospital readmission rates has led health systems to develop predictive models that allow for targeting of high-risk patients prior to discharge. Structure data from the electronic health record are often used to create these predictive models; however involving only a subset of clinical features where prediction is likely to be accurate may yield superior model performance at the expense of generalizability to other patient populations or care settings. Although logistic regression is seen as a natural choice for this modeling task due its probabilistic structure, modern statistical learning has proposed a variety of methods such as support vector machines and gradient-boosted trees that provide comparable or improved performance on standard testing criteria; with large amounts of patient data being collected, these approaches can be applied simply. Nevertheless, the rationale for categorical or non-linear methods over properly regularized logistic regression remains insufficiently understood. A comprehensive predictive modeling framework for identifying patients at high risk of hospital readmission based entirely on structured clinical data. Five methods—logistic regression with and without lasso and ridge regularization, support vector machines with a linear or radial basis function kernel, and gradient-boosted trees—were used and their predictions compared. Models were trained on admissions from a 3-year period and external validation performed using the 4th year. A stepwise variable selection approach was also investigated, aiming to identify a restricted subset of clinical data for which prediction would be accurate. Model performance was evaluated using area under the receiver-operator characteristic curve, calibration plots, and positive predictive value. Results indicated that predictive models for hospital readmission could be developed on the entire patient population in a general hospital, while maintaining sufficient performance characteristics for use in practice.
Copyright (c) 2023 European Journal of Analytics and Artificial Intelligence (EJAAI)
Generative AI for Secure Real-Time Financial Transaction Governance in the Cloud
Financial governance may benefit from generative AI as it promotes augmented governance by design. The resulting capability is operationalized as a blueprint for real-time secure transaction processing in cloud ecosystems. Through the design and integration of the various system layers, desired characteristics of transparency, explainability, auditability, and trust become built-in features that support and enhance the risk management, control, and decision-making processes of stakeholders across the value network by providing and assuring consistent information, even in the presence of uncertainty. Generative AI is applied to define the decision-making process, specifying how the Generative AI Governance Decision Proposal defines financial policy and compliance. Consequently, the related sense—analyze—respond model of augmented governance identifies the roles, responsibilities, decision rights, escalation paths, and governance rituals that provide the operational framework for governance. Data ingestion pipelines enrich the provenance of information to support enhanced risk–control–accountability. Transparency is specified from a governance perspective, defining policy alignment for trust in the AI–enabled financial decision outcomes.