Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Konstantin Sharafutdinov"'
Autor:
Moein E. Samadi, Jorge Guzman-Maldonado, Kateryna Nikulina, Hedieh Mirzaieazar, Konstantin Sharafutdinov, Sebastian Johannes Fritsch, Andreas Schuppert
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-21 (2024)
Abstract The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient’s condition or prognosi
Externí odkaz:
https://doaj.org/article/fb473d74a042418b9eb0ef2e1b859041
Autor:
Konstantin Sharafutdinov, Sebastian Johannes Fritsch, Mina Iravani, Pejman Farhadi Ghalati, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology, Vol 5, Pp 611-620 (2024)
Goal: Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their
Externí odkaz:
https://doaj.org/article/44befcf235114feabf6f4ed4938e85a4
Publikováno v:
SoftwareX, Vol 23, Iss , Pp 101517- (2023)
Setting up data structures, parallelizing code, and creating visualizations are tasks in almost any project aiming to develop healthcare AI solutions based on heterogeneous, high-dimensional data structures. While toolkits for individual parts of thi
Externí odkaz:
https://doaj.org/article/11b8706dc95b45aaa9fe27a9e8b81029
Autor:
Konstantin Sharafutdinov, Sebastian Johannes Fritsch, Gernot Marx, Johannes Bickenbach, Andreas Schuppert
Publikováno v:
BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-9 (2021)
Abstract Background The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find po
Externí odkaz:
https://doaj.org/article/f8bfb3e4f47749f882cb6485eab04ac6
Autor:
Konstantin Sharafutdinov, Jayesh S. Bhat, Sebastian Johannes Fritsch, Kateryna Nikulina, Moein E. Samadi, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data then generalization error is considered high. Thi
Externí odkaz:
https://doaj.org/article/4c8c914b67c34e4eba51ceced1d34906
Autor:
Chadi S. Barakat, Konstantin Sharafutdinov, Josefine Busch, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Andreas Schuppert, Sigurður Brynjólfsson, Sebastian Fritsch, Morris Riedel
Publikováno v:
Diagnostics, Vol 13, Iss 12, p 2098 (2023)
Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, alt
Externí odkaz:
https://doaj.org/article/68f993e2e5b9465ea5347f526a18c844
Autor:
Stefan Kluge, Christian Putensen, Danny Ammon, Silke Haferkamp, Saskia Deffge, Thomas Wendt, Daniel Tiller, André Scherag, Philipp Simon, Johannes Bickenbach, Sebastian Johannes Fritsch, Julian Benedict Kunze, Oliver Maassen, Jennifer Kistermann, Irina Lutz, Nora Kristiana Voellm, Volker Lowitsch, Richard Polzin, Konstantin Sharafutdinov, Hannah Mayer, Lars Kuepfer, Rolf Burghaus, Walter Schmitt, Joerg Lippert, Morris Riedel, Chadi Barakat, André Stollenwerk, Simon Fonck, Sven Zenker, Felix Erdfelder, Daniel Grigutsch, Rainer Kram, Susanne Beyer, Knut Kampe, Jan Erik Gewehr, Friederike Salman, Patrick Juers, Emilia Wisotzki, Sebastian Gross, Lorenz Homeister, Frank Bloos, Susanne Mueller, Julia Palm, Nora Jahn, Markus Loeffler, Tobias Schuerholz, Petra Groeber, Andreas Schuppert
Publikováno v:
BMJ Open, Vol 11, Iss 4 (2021)
Introduction The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at th
Externí odkaz:
https://doaj.org/article/16921f71636846b9984cf353bd67fb35
Autor:
Konstantin Sharafutdinov, Sebastian Johannes Fritsch, Mina Iravani, Pejman Farhadi Ghalati, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology. :1-11
GoalMachine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their u
Autor:
Riedel, Chadi S. Barakat, Konstantin Sharafutdinov, Josefine Busch, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Andreas Schuppert, Sigurður Brynjólfsson, Sebastian Fritsch, Morris
Publikováno v:
Diagnostics; Volume 13; Issue 12; Pages: 2098
Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, alt
Publikováno v:
Anasthesiologie, Intensivmedizin, Notfallmedizin, Schmerztherapie : AINS. 57(3)
The COVID-19 pandemic is a global health emergency of historic dimension. In this situation, researchers worldwide wanted to help manage the pandemic by using artificial intelligence (AI). This narrative review aims to describe the usage of AI in the