TIMELY Results

Douma, E. et al. (2024). Patient-reported preferences in eHealth-based cardiac rehabilitation: A qualitative investigation of behavior change techniques, barriers and facilitators.

Gendre, B. et al. (2024). A genome wide search for non-additive allele effects identifies PSKH2 as involved in the variability of Factor V activity.

Molnar, S. et al. (2024). Clinical and genetic diagnosis of familial hypercholesterolaemia in patients undergoing coronary angiography: the Ludwigshafen Risk and Cardiovascular Health Study.

Schmitz, B. et al. (2024). Living Lab Data of Patient Needs and Expectations for eHealth-Based Cardiac Rehabilitation in Germany and Spain From the TIMELY Study: Cross-Sectional Analysis.

Schäfer, H. et al. (2023). Altered tissue oxygenation in patients with post COVID-19 syndrome.

Mooren, J. M. et al. (2023). Medical Rehabilitation of Patients with Post-COVID-19 Syndrome—A Comparison of Aerobic Interval and Continuous Training.

Heimer, M. et al. (2023). eHealth for maintenance cardiovascular rehabilitation: a systematic review and meta-analysis.

Hammer, A. et al. (2023). Cardiovascular Reflections of Sympathovagal Imbalance Precede the Onset of Atrial Fibrillation.

Krämer, R.M. et al. (2023). High genetic risk for depression as an independent risk factor for mortality in patients referred for coronary angiography.

Tsarapatsani, K.-H. et al. (2023). Predicting the death caused by cardiovascular and/or cerebrovascular disease within 7 years follow-up using machine learning models.

Tsarapatsani, K.-H. et al. (2023). Prediction of all-cause mortality in cardiovascular patients using machine learning models.

Tsarapatsani, K.-H. et al. (2023). Machine Learning Model Predict Fatal Myocardial Infarction Within 10-years Follow-up Utilizing Explainable AI.

Berger, M. et al. (2023). Platelet Reactivity and Cardiovascular Mortality Risk in the LURIC Study.

Tsarapatsani, K.-H. et al. (2023). Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models.

Tsarapatsani, K.-H. et al. (2023). Machine Learning Models Predict the Need of Amputation and/or Peropheral Artery Revascularization in Hypertensive Patients Within 7-Years Follow-Up.

Schmitz, B. (2023). Improving accessibility of scientific research by artificial intelligence - An example for lay abstract generation. DIGITAL HEALTH.

Mooren, F. C. et al. (2023). Autonomic dysregulation in long-term patients suffering from Post-COVID-19 Syndrome assessed by heart rate variability.

Schmitz, B. & Mooren, F. (2022). TIMELY – Eine patientenzentrierte Plattform zur Früherkennung, Prävention und Intervention bei koronarer Herzkrankheit mithilfe von eHealth und künstlicher Intelligenz.

Schäfer, H. et al. (202A). AI-powered Lifestyle Intervention for Patient-Centered Cardiac Rehabilitation – the TIMELY Approach.

Hammer, A. et al. (2022). Towards the Prediction of Atrial Fibrillation Using Interpretable ECG Features.

Schmitz, B. et al. (2022). Patient-centered cardiac rehabilitation by AI-powered lifestyle intervention – the timely approach.

Tsarapatsani, K.-H. et al. (2022). Machine Learning Models for Cardiovascular Disease Events Prediction.

Heimer, M. et al. (2022). Health benefits of probiotics in sport and exercise—Non-existent or a matter of heterogeneity? A systematic review.

Schmitz, B. et al. (2022). Defining patients needs and expectations for eHealth-based cardiac rehabilitation in Germany and Spain: living lab data from the TIMELY study.

Hammer, A. et al. (2021). Automatic Classification of Full- and Reduced-Lead Electrocardiograms Using Morphological Feature Extraction.

Hammer, A. et al. (n.d.). Automatic Classification of 2- , 3-, 4-, 6- , and 12-Lead Electocardiograms Using Morphological Feature Extraction.