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Background: Evidence-based treatment recommendations are helpful in the corresponding discipline-specific treatment but can hardly take data from real-world care into account. In order to make better use of this in everyday clinical practice, including with respect to predictive statements about disease development or treatment success, models with data from treatment must be developed in order to use them for the development of assistive artificial intelligence.
Goal: The aim of the Use Case 1 of the medical informatics hub in Saxony (MiHUBx) is the development of a model based on treatment and research data for a treatment algorithm supported by biomarkers and also the development of the necessary digital infrastructure.
Material And Methods: Step by step, the necessary partners in hospitals and practices will be brought together technically or through research questions within Use Case 1 "Ophthalmology meets Diabetology", a regional digital progress hub in health, the medical informatics hub in Saxony (MiHUBx ) of the nationwide medical informatics initiative (MII).
Results: Based on joint studies with diabetologists, robust serological and imaging biomarkers were selected that provide evidence of the development of diabetic macular edema (DME). In the future, these and other scientifically proven prognostic markers will be incorporated into a treatment algorithm that is supported by artificial intelligence (AI). For this purpose, model procedures are being developed together with medical informatics specialists. At the same time, a data integration center (DIZ) was established.
Conclusion: In addition to the structured and technical combination of the previously disseminated and partially heterogeneous treatment data, the Use Case 1 defines the chances and hurdles for using such real-world data to develop artificial intelligence.
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http://dx.doi.org/10.1007/s00347-024-02146-x | DOI Listing |
Alzheimers Dement
September 2025
Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Introduction: We compared and measured alignment between the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard used by electronic health records (EHRs), the Clinical Data Interchange Standards Consortium (CDISC) standards used by industry, and the Uniform Data Set (UDS) used by the Alzheimer's Disease Research Centers (ADRCs).
Methods: The ADRC UDS, consisting of 5959 data elements across eleven packets, was mapped to FHIR and CDISC standards by two independent mappers, with discrepancies adjudicated by experts.
Results: Forty-five percent of the 5959 UDS data elements mapped to the FHIR standard, indicating possible electronic obtainment from EHRs.
Obesity (Silver Spring)
September 2025
Division of Hematology, Oncology, and Palliative Care, School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA.
Objective: From October 18-20, 2022, the National Institutes of Health held a workshop to examine the state of the science concerning obesity interventions in adults to promote health equity. The workshop had three objectives: (1) Convene experts from key institutions and the community to identify gaps in knowledge and opportunities to address obesity, (2) generate recommendations for obesity prevention and treatment to achieve health equity, and (3) identify challenges and needs to address obesity prevalence and disparities, and develop a diverse workforce.
Methods: A three-day virtual convening.
The morphological patterns of lung adenocarcinoma (LUAD) are recognized for their prognostic significance, with ongoing debate regarding the optimal grading strategy. This study aimed to develop a clinical-grade, fully quantitative, and automated tool for pattern classification/quantification (PATQUANT), to evaluate existing grading strategies, and determine the optimal grading system. PATQUANT was trained on a high-quality dataset, manually annotated by expert pathologists.
View Article and Find Full Text PDFEur Heart J Open
September 2025
Calderdale and Huddersfield NHS Foundation Trust, Acre St, Lindley, Huddersfield HD3 3EA, UK.
Aims: Cardiogenic shock remains a significant cause of mortality despite multiple advancements in medical interventions. Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) provides crucial circulatory support but also increases left ventricular (LV) after-load, potentially worsening outcomes. Effective LV unloading strategies can enhance patient survival during VA-ECMO treatment.
View Article and Find Full Text PDFAntimicrob Steward Healthc Epidemiol
September 2025
Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
EvalHCID is a clinical decision support system integrating outbreak intelligence, symptom onset, and epidemiologic risk factors to identify high consequence infectious diseases (HCIDs) (eg, Ebola). Tested among 20 emergency department (ED) providers, it significantly reduced assessment time, lowered misclassification, and scored "excellent" usability. EvalHCID may improve institutional preparedness and patient outcomes for emerging infectious disease threats.
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