- Evaluation of short- and long-term mortality prediction in patients undergoing hip fracture surgery using a biomarker-based interpretable predictive modeling approach: a retrospective cohort analysis. [Journal Article]BMC Med Inform Decis Mak. 2026 Sep 23; 26(1).BM
- CONCLUSIONS: The biomarker-based interpretable predictive modeling approach developed in this study demonstrated acceptable discriminative performance for estimating short- and long-term mortality after hip fracture surgery. Advanced age, elevated NLR, and increased LAR emerged as important predictors. As these variables are routinely available in clinical practice, the proposed framework may support perioperative risk awareness and assist clinical evaluation when interpreted together with comprehensive clinical assessment.
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- FAIR by design (TRACE): A Trusted Research Access & Collaboration Environment. [Journal Article]
- CONCLUSIONS: Platforms like TRACE may help shift resource-intensive, expert-dependent data governance towards automated processes. Operated by existing institutional staff, TRACE may make controlled data sharing feasible for institutions with limited resources. By automating previously manual steps, TRACE is intended to reduce the administrative burden of governed data sharing; whether it shortens the interval from data request to analysis has not been measured. TRACE is used as a training environment for preparing and using data in sharing processes.
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- A framework for extraction of clinical information from radiological mammography reports using large language models and retrieval augmented generation. [Journal Article]
- CONCLUSIONS: Few-shot RAG combined with modern LLMs provides a viable, data-efficient alternative for structuring Spanish mammography reports, particularly in low-resource or rapid-deployment settings. While fine-tuned encoders remain preferable for high-accuracy RE, the proposed framework offers a practical balance between performance, operational cost, and accessibility. A remaining limitation is the need for an initial annotated subset to populate the RAG store; future work will explore weak supervision, multimodal extensions, and cross-site generalization.
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- Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data. [Systematic Review]
- CONCLUSIONS: ML-assisted pharmacovigilance enables a shift from passive to active, intelligent monitoring. Despite challenges in data quality, model interpretability, and regulatory approval, intelligent pharmacovigilance systems will become essential infrastructure for safeguarding public health.
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- Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus. [Systematic Review]
- CONCLUSIONS: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.
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- Development and external validation of a machine-learning risk model for colorectal cancer triage in Sweden's fast-track pathway. [Journal Article]
- CONCLUSIONS: A multivariable approach using FIT, simple laboratory data, and clinical findings can support triage in a fast-track pathway. In validation, discrimination was modest, and superiority over the entry rule could not be shown because all referred patients underwent colonoscopy. External testing across regions, recording quantitative FIT, and routine recalibration are needed before implementation.
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- Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy. [Journal Article]
- CONCLUSIONS: The TyG-BMI and TC/HDL-C ratio independently predict ICM risk, with the XGB model identified as the most effective for ICM risk prediction, indicating substantial clinical applicability.
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- Decentralized rare disease studies in Germany: first results and hurdles of secondary use of patient data. [Journal Article]
- CONCLUSIONS: Naming the hurdles enables the identification of areas for improvements, which will be the base for the development of new approaches or adaptations of existing tools and methodologies for the future. Although adaptation would make an impact, the initial results already show that decentralized analyses based on secondary use of patient data can improve research and thus also the care for people with rare diseases.
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- The word and the way: strategies for domain-specific BERT pre-training in German medical NLP. [Journal Article]
- CONCLUSIONS: ChristBERT establishes a new state-of-the-art for German clinical language modeling. Our findings indicate that the optimal domain adaptation strategy is task-dependent and remains crucial, as adapted models consistently outperformed general-purpose language models in our experiments. To support further research and application in German medical NLP, all developed models are publicly released.
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- GERBEHRT: a BERT-based model tailored for German electronic health records - potential in chronic kidney disease prediction. [Journal Article]
- CONCLUSIONS: Predicting moderate-to-severe CKD based on real-world EHRs remains challenging. However, our proposed architecture was able to make more accurate predictions than traditional approaches and feature sets, underscoring the importance of comprehensive EHR utilization and the potential of tailored deep learning models for personalized CKD risk prediction and targeted patient screening.
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- Provider perspectives of clinical decision support for chronic kidney disease management: a cross-sectional study. [Journal Article]BMC Med Inform Decis Mak. 2026 Jul 21. [Online ahead of print]BM
- CONCLUSIONS: Clinicians support CDS integration to enhance CKD care in ambulatory settings. The proposed CDS increased perceived prescribing confidence, particularly for GLP-1RA and nsMRA.
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- Based on the middle ear negative pressure and multimodal data to construct and externally validate the predictive model for pediatric obstructive sleep apnea. [Journal Article]BMC Med Inform Decis Mak. 2026 Jul 21. [Online ahead of print]BM
- CONCLUSIONS: Based on a large sample size and multivariate analysis of factors associated with pediatric OSA, we developed a predictive model incorporating middle ear negative pressure for pediatric OSA, which may assist clinicians in diagnosing pediatric OSA in complex clinical settings.
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- Reducing overconfident errors in clinical prediction models. [Journal Article]BMC Med Inform Decis Mak. 2026 Jul 20. [Online ahead of print]BM
- Machine Learning (ML) models are increasingly being used in clinical workflows. Evaluation of these models tends to focus on global performance metrics, which can obscure error patterns and lead to bias in clinical decision making. Here, we propose Proximal Error-Based Confidence Adjustment (PECA), a framework designed explicitly to improve the safety of ML predictions by reducing a model's confi…
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- Clinical decision support system for child and adolescent mental health services: a formative usability study. [Journal Article]BMC Med Inform Decis Mak. 2026 Jul 20. [Online ahead of print]BM
- CONCLUSIONS: The scenario-based exploration of the IDDEAS 1.0 prototype allowed CAMHS clinicians to offer honest reflections about receiving decision-support, and what they might potentially need for such support to enhance their clinical decision-making and information processing at the point of care. Functional adjustments in IDDEAS were recommended, with a focus on workflow cohesion and individualized adaptability for optimal ease of use and personalized patient care.
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