Background Infusion pumps are used in critical care settings to deliver medicines such as norepinephrine (NE), insulin, and certain nutrients. Currently, clinicians preset the infusion rate in the device, usually for 1 hour, resulting in a constant drug delivery. However, a patient's response to a preset infusion can vary over time depending on their vital changes. In the case of NE, dosage errors are reported due to delayed infusion and interpatient variability in hemodynamics. To automate the infusion process, fuzzy logic, and proportional integral derivative controllers have been used, but they have fixed algorithms and lack clinical validation. Objectives This study proposes a data-driven artificial intelligence (AI)-digital twin adaptive framework for NE infusion to reduce the risks of hypotension and hypertension. Methods For personalized infusion, data were collected from patients supported with NE. A random forest decision model (RFDM) was used to predict the NE rates based on varying hemodynamics. The model performance at each time step is evaluated using a long short-term memory network that works as an adaptive digital twin cardiac model (DTCM). Virtual automation of the infusion process is performed using outputs of AI models. Results The RFDM achieved strong predictive accuracy, with a mean absolute percentage error (MAPE) of 5.3% and an R 2 score of 0.97. MAPE values of DTCM for systolic, diastolic, and mean arterial blood pressure (BP) were 5.56, 5.79, and 5.36%, respectively. Compared with real-world infusion, the virtual automation reduced drug consumption by 13.82%. Conclusion The adaptive DTCM resembles real-world hemodynamics and has improved performance across various infusion rates. RFDM combined with DTCM provides an alternative method for NE infusion. The simulated evaluation shows that the proposed framework maintains BP, automates the infusion process, enabling patient-specific personalized dosing.
Background Cancer remains the leading cause of disease-related mortality in children over the age of one in Europe, with over 35,000 new pediatric cases and more than 6,000 deaths annually. Due to the rarity of pediatric cancers, clinical trial protocols often substitute for formal treatment guidelines, resulting in many children being enrolled in multiple trials, with biological samples and genomic data stored in various biobanks. Data collection in pediatric oncology is challenging, with sparse data acquired over extended periods, underscoring the need for optimal utilization of all available information through linked, privacy-preserving datasets. Methods Here, we report the development of a distributed, privacy-preserving data infrastructure for the PRIMAGE project, a European initiative aimed at supporting artificial intelligence (AI)-driven image analysis for pediatric cancer prognostics. The infrastructure leverages the European Patient Identity (EUPID) Services for Privacy-Preserving Record Linkage, enabling pseudonymized data integration across clinical, biological, and imaging sources. The system incorporates EUPID's hashing and phonetic matching protocols to pseudonymize patient identifiers and link distributed datasets, facilitating secondary data use in compliance with the General Data Protection Regulation. Results Data from over 700 neuroblastoma patients from European trials and hospitals were linked and uploaded to the PRIMAGE platform, where AI models predict clinical outcomes. Conclusion This infrastructure successfully facilitated AI model development, advancing pediatric oncology research, and offering a scalable framework for future European health data initiatives, such as the European Health Data Space.
Background:Biomedical informatics increasingly reuses clinical artificial intelligence (AI) benchmarks, yet successor releases are often treated as independent external corpora. If the same participants reappear under new corpus names, features, or partition files, evaluation can become internal while reported as external. Objectives:The objective of this study is to define successor-release participant leakage as a benchmark-integrity threat, operationalize a preevaluation audit and leak-free protocol, and demonstrate its impact using the Distress Analysis Interview Corpus, Wizard-of-Oz condition (DAIC-WOZ), and the Extended DAIC (E-DAIC) depression-screening benchmarks. Methods:We audited release lineage and participant identity using persistent identifiers, content hashing, Patient Health Questionnaire-8 (PHQ-8) label reconciliation, and fold-transition cross-tabulation. We compared leaky and participant-disjoint E-DAIC-to-DAIC-WOZ protocols on the same heldout participants using paired bootstrap inference and derived conservative leak-free reference baselines across standard pipelines. Results:All 189 DAIC-WOZ participants reappeared in E-DAIC with byte-identical recordings and identical PHQ-8 totals. Across official partitions, 104 of 189 changed fold. Training on E-DAIC and evaluating on the DAIC-WOZ test set placed 47/47 test participants in the model-development pool; the reverse direction was clean. The leaky acoustic protocol yielded area under the receiver operating characteristic curve (AUROC) 0.797 versus 0.569 after deduplication, with paired Delta AUROC + 0.227 (95% confidence interval + 0.106 to + 0.372; two-sided p < 0.001). At matched training size, Delta AUROC was + 0.249. A generic visual classifier reached AUROC 0.887 on a blended external set but 0.562 on the unseen AI-only portion. Leak-free reference baselines were approximately AUROC of 0.60. Conclusion:Successor-release participant leakage is a preventable evaluation failure in clinical AI benchmark reuse. Before pooling, external validation, or model comparison, biomedical informatics studies should document release lineage, verify participant identity at the signal or record level, reconcile labels, deduplicate across releases, and enforce participant-disjoint evaluation.
Background:Biomedical informatics plays a central role in supporting learning health systems (LHSs) that aim to continuously improve care by transforming data into knowledge and knowledge into action. However, health care data reflect existing disparities in access, delivery, and outcomes, raising concerns about whether the LHS cycle can improve care equitably without intentional design. Objective:This study aims to describe the recurring theme of health equity found in recent biomedical informatics literature, using a convenience sample of articles contributed to the 2025 American Medical Informatics Association (AMIA) Year in Review (YIR). Methods:In developing the 2025 AMIA YIR presentation, we found that 240 articles submitted by AMIA experts could be organized into multiple themes to allow summarization and synthesis for efficient presentation of 140 of them. Many themes could themselves be organized around the concept of the LHS, which further streamlined the presentation. We were struck by how frequently biomedical informatics literature addressed health equity in relation to LHSs. We therefore applied our own expertise to distill relevant articles from the larger set to explore this recurrent issue. Results:Here, we present 56 papers selected from 9 of the 19 themes that illustrate how biomedical informatics can support equity at each stage of the LHS. Mature LHS implementations provided large-scale examples of how learning cycles can be designed for equity. Real-World Evidence and Inclusive Data themes showed that equitable learning depends on whether data are complete enough to represent the populations the system intends to serve. Natural language processing and artificial intelligence (AI) modeling showed how data are transformed into knowledge and translated into clinical action through decision support. Trust and justice in data use, health equity as infrastructure, and equitable access emerged as interdependent conditions that influence whether the learning cycle operates equitably and effectively. Conclusion:Equity emerged as a condition influencing every stage of the LHS cycle. The value of informatics interventions cannot be judged by technical performance alone, but by whether they build learning cycles that are inclusive, accountable, and equitable for the populations they serve.
