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ConsensusPathDB combines molecular interaction data from multiple sources and provides a web interface to vizualize these data. This can be used to build interaction networks and to perform enrichment/over-representation analysis. ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB ( http://consensuspathdb.org ) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.

Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. ClinicalTrials.gov Identifier: NCT02865967.

Artificial intelligence (AI) has the potential to revolutionize health care by enhancing both clinical outcomes and operational efficiency. However, its clinical adoption has been slower than anticipated, largely due to the absence of comprehensive evaluation frameworks. Existing frameworks remain insufficient and tend to emphasize technical metrics such as accuracy and validation, while overlooking critical real-world factors such as clinical impact, integration, and economic sustainability. This narrow focus prevents AI tools from being effectively implemented, limiting their broader impact and long-term viability in clinical practice. This study aimed to create a framework for assessing AI in health care, extending beyond technical metrics to incorporate social and organizational dimensions. The framework was developed by systematically reviewing, analyzing, and synthesizing the evaluation criteria necessary for successful implementation, focusing on the long-term real-world impact of AI in clinical practice. A search was performed in July 2024 across the PubMed, Cochrane, Scopus, and IEEE Xplore databases to identify relevant studies published in English between January 2019 and mid-July 2024, yielding 3528 results, among which 44 studies met the inclusion criteria. The systematic review followed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines and the Cochrane Handbook for Systematic Reviews. Data were analyzed using NVivo through thematic analysis and narrative synthesis to identify key emergent themes in the studies. By synthesizing the included studies, we developed a framework that goes beyond the traditional focus on technical metrics or study-level methodologies. It integrates clinical context and real-world implementation factors, offering a more comprehensive approach to evaluating AI tools. With our focus on assessing the long-term real-world impact of AI technologies in health care, we named the framework AI for IMPACTS. The criteria are organized into seven key clusters, each corresponding to a letter in the acronym: (1) I-integration, interoperability, and workflow; (2) M-monitoring, governance, and accountability; (3) P-performance and quality metrics; (4) A-acceptability, trust, and training; (5) C-cost and economic evaluation; (6) T-technological safety and transparency; and (7) S-scalability and impact. These are further broken down into 28 specific subcriteria. The AI for IMPACTS framework offers a holistic approach to evaluate the long-term real-world impact of AI tools in the heterogeneous and challenging health care context and lays the groundwork for further validation through expert consensus and testing of the framework in real-world health care settings. It is important to emphasize that multidisciplinary expertise is essential for assessment, yet many assessors lack the necessary training. In addition, traditional evaluation methods struggle to keep pace with AI's rapid development. To ensure successful AI integration, flexible, fast-tracked assessment processes and proper assessor training are needed to maintain rigorous standards while adapting to AI's dynamic evolution. reviewregistry1859; https://tinyurl.com/ysn2d7sh.

In low resource settings recall of the date of the mother's last menstrual period may be unreliable and due to limited availability of prenatal ultrasound, gestational age of newborns may not be assessed reliably. Preterm babies are at high risk of morbidity and mortality so an alternative strategy is to identify them soon after birth is needed for early referral and management. The objective of this study was to assess the accuracy in assessing prematurity of newborn, over and above birthweight, using a pictorial Simplified Gestational Age Score adapted for use as a Tablet App. Two trained nurse midwives, blinded to each other's assessment and the actual gestational age of the baby used the app to assess gestational age at birth in 3 hospitals based on the following 4 parameters-newborn's posture, skin texture, breast and genital development. Inter-observer variation was evaluated and the optimal scoring cut-off to detect preterm birth was determined. Sensitivity and specificity of gestational age score using the tablet was estimated using combinations of last menstrual period and ultrasound as reference standards to assess preterm birth. The predictive accuracy of the score using the area under a receiver operating characteristic curve was also determined. To account for potential reference standard bias, we also evaluated the score using latent class models. A total of 8,591 live singleton births whose gestational age by last menstrual period and ultrasound was within 1 weeks of each other were enrolled. There was strong agreement between assessors (concordance correlation coefficient 0.77 (95% CI 0.76-0.78) and Fleiss' kappa was 0.76 (95% CI 0.76-0.78). The optimal cut-off for the score to predict preterm was 13. Irrespective of the reference standard, the specificity of the score was 90% and sensitivity varied from 40-50% and the predictive accuracy between 74%-79% for the reference standards. The likelihood ratio of a positive score varied between 3.75-4.88 while the same for a negative likelihood ratio consistently varied between 0.57-0.72. Latent class models showed similar results indicating no reference standard bias. Gestational age scores had strong inter-observer agreement, robust prediction of preterm births simplicity of use by nurse midwives and can be a useful tool in resource-limited scenarios.

Chronic illnesses are significant to individuals and costly to society. When systematically implemented, the well-established and tested Chronic Care Model (CCM) is shown to improve health outcomes for people with chronic conditions. Since the development of the original CCM, tremendous information management, communication, and technology advancements have been established. An opportunity exists to improve the time-honored CCM with clinically efficacious eHealth tools. The first goal of this paper was to review research on eHealth tools that support self-management of chronic disease using the CCM. The second goal was to present a revised model, the eHealth Enhanced Chronic Care Model (eCCM), to show how eHealth tools can be used to increase efficiency of how patients manage their own chronic illnesses. Using Theory Derivation processes, we identified a \"parent theory\", the Chronic Care Model, and conducted a thorough review of the literature using CINAHL, Medline, OVID, EMBASE PsychINFO, Science Direct, as well as government reports, industry reports, legislation using search terms \"CCM or Chronic Care Model\" AND \"eHealth\" or the specific identified components of eHealth. Additionally, \"Chronic Illness Self-management support\" AND \"Technology\" AND several identified eHealth tools were also used as search terms. We then used a review of the literature and specific components of the CCM to create the eCCM. We identified 260 papers at the intersection of technology, chronic disease self-management support, the CCM, and eHealth and organized a high-quality subset (n=95) using the components of CCM, self-management support, delivery system design, clinical decision support, and clinical information systems. In general, results showed that eHealth tools make important contributions to chronic care and the CCM but that the model requires modification in several key areas. Specifically, (1) eHealth education is critical for self-care, (2) eHealth support needs to be placed within the context of community and enhanced with the benefits of the eCommunity or virtual communities, and (3) a complete feedback loop is needed to assure productive technology-based interactions between the patient and provider. The revised model, eCCM, offers insight into the role of eHealth tools in self-management support for people with chronic conditions. Additional research and testing of the eCCM are the logical next steps.

The integration of artificial intelligence (AI) into clinical workflows is advancing even before full compliance with the European Union Cross-Border eHealth Network (MyHealth@EU) framework is achieved. While AI-based clinical decision support systems are automatically classified as high risk under the European Union’s AI Act, cross-border health data exchange must also satisfy MyHealth@EU interoperability requirements. This creates a dual-compliance challenge: vertical safety and ethics controls mandated by the AI Act and horizontal semantic transport requirements enforced through Open National Contact Point (OpenNCP) gateways, many of which are still maturing toward production readiness. This paper provides a practical, phase-oriented tutorial that enables developers and providers to embed AI Act safeguards before approaching MyHealth@EU interoperability tests. The goal is to show how AI-specific metadata can be included in the Health Level Seven International Clinical Document Architecture and Fast Healthcare Interoperability Resources messages without disrupting standard structures, ensuring both compliance and trustworthiness in AI-assisted clinical decisions. We systematically analyzed Regulation (EU) 2024/1689 (AI Act) and the OpenNCP technical specifications, extracting a harmonized set of overlapping obligations. The AI Act provisions on transparency, provenance, and robustness are mapped directly onto MyHealth@EU workflows, identifying the points where outgoing messages must record AI involvement, log provenance, and trigger validation. To operationalize this mapping, we propose a minimal extension set, covering AI contribution status, rationale, risk classification, and Annex IV documentation links, together with a phase-based compliance checklist that aligns AI Act controls with MyHealth@EU conformance steps. A simulated International Patient Summary transmission demonstrates how Clinical Document Architecture/Fast Healthcare Interoperability Resources extensions can annotate AI involvement, how OpenNCP processes such enriched payloads, and how clinicians in another member state view the result with backward compatibility preserved. We expand on security considerations (eg, Open Worldwide Application Security Project generative AI risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU’s incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies. AI-enabled clinical software succeeds only when AI Act safeguards and MyHealth@EU interoperability rules are engineered together from day 0 . This tutorial provides developers with a forward-looking blueprint that reduces duplication of effort, streamlines conformance testing, and embeds compliance early. While the concept is still in its early phases of practice, it represents a necessary and worthwhile direction for ensuring that future AI-enabled clinical systems can meet both European Union regulatory requirements from day 1. risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU’s incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies.

* Context.--Acquiring objective, timely, and comprehensive feedback on resident diagnostic performance is notoriously difficult. Objective.--To implement a custom software application (Resident Case Tracker) to improve evaluative diagnostic analysis for residency programs. Design.--Residents and faculty use a graphical user interface with restricted access to their own cases and evaluations. For each sign-out, residents enter their diagnoses and comments for each case. Faculty are provided a sign-out queue to review the resident diagnosis and select their level of agreement alongside optional comments. After sign-out, residents can review the agreement level and comments for each case, overall sign-out statistics, and organ-specific performance, and they have the option of opening and reviewing groups of cases by agreement status. A sign-out evaluation is automatically generated and stored alongside additional reports. Administrative access allows privileged users to readily review data analytics at both an individual and residency-wide global level. Results.--A marked increase in completed evaluations and feedback was noted in the initial 36 months of implementation. During a 3-year academic period, faculty completed individual feedback on 33 685 cases and 1073 overall sign-out evaluations. Conclusions.--Resident Case Tracker is an invaluable tool for our residency program and has provided unparalleled feedback and data analytics. Throughout residency, trainees have access to each completed sign-out, with the ability to learn from discrepant cases while also monitoring improvements in diagnostic acumen over time. Faculty are able to assess resident milestones much more effectively while more readily identifying residents who would benefit from targeted study.

Background Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for each patient accurately and individually. However, physicians can reduce their workload by using recommender systems, still having every decision under control. In this way, they also get an insight into how other physicians make treatment decisions in each situation. In this work, we report the development of a novel recommender system that uses predicted outcomes based on continuous-valued logic and multi-criteria decision operators. The advantage of this methodology is that it is transparent, since the model outcomes emulate logical decision processes based on the hierarchy of relevant physiological parameters, and second, it is safer against adversarial attacks than conventional deep learning methods since it drastically reduces the number of trainable parameters. Methods We test our methodology in a patient population with diabetes and heart insufficiency that becomes a therapy (beta-blockers, ACE or Aspirin). The original database (Pakistan database) is publicly available and accessible via the internet. However, to explore methods to protect the patient's identity and guarantee data privacy we implemented a methodology on a variable-by-variable basis by fitting a sequence of regression models and drawing synthetic values from the corresponding predictive distributions using linear regressions and norm rank. Furthermore, we implemented a deep-learning model based on logical gates modeled by perceptrons with fixed weights and biases. While a first trainable layer automatically recognizes a meaningful parameter hierarchy, the implemented Logic-Operator Neuronal Network (LONN) simulates cognitive processes like a rational, logical thinking process, considering that this logic is joined by fuzziness, i.e., logical operations are not exact but essentially fuzzy due to the implemented continuous-valued operators. The predicted outcomes of the model (kind of therapy-ACE, Aspirin or beta-blocker- and expected therapy time of the patient) are then implemented in a recommender system that compares two different models: model 1 trained on a population excluding negative outcomes (patient group 1, with no patient dead and long therapy times) and a model 2 trained on the whole patient population (patient group 2). In this way, we provide a recommendation of the best possible therapy based on the outcome of the model and the confidence of this recommendation when the outcome of model 1 is compared with the outcome of model 2. Results With the applied method for data synthetization, we obtained an error of about 1% for all the relevant parameters. Furthermore, we demonstrate that the LONN models reach an accuracy of about 75%. After comparing the LONN models against conventional deep-learning models we observe that our implemented models are less accurate (accuracy loss of about 8%). However, the loss of accuracy is compensated by the fact that LONN models are transparent and safe because the freezing of training parameters makes them less prone to adversarial attacks. Finally, we predict the best therapy as well as the expected therapy time. We were able to predict individualized therapies, which were classified as optimal (binary value) when the prediction fully matched predictions made with models 1 and 2. The results provided by the recommender system are displayed using a graphical interface. The current is a proof of concept to improve the quality of the disease management, while the methods are continuously visualized to preserve transparency for the customers. Conclusions This work contributes to simplify administrative functions and boost the quality of management of patients improving the quality of healthcare with models that are both transparent and safe. Our methodology can be extended to different clinical scenarios where recommender systems can be applied. The acceptance and further development of the app is one of the next important steps and still requires further development depending on specific requirements of the health management, the physicians or health professionals, and the patent population.

Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke. We utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia. In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78. A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.