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Autor:
Tong Wang, Mark S. Anderson, Roland G. Henry, Summit Investigators, Bruce A.C. Cree, Hrishikesh Lokhande, Tanuja Chitnis, Riley Bove, Howard L. Weiner, Yijun Zhao, Rohit Bakshi, Mariann Polgar-Turcsanyi
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
NPJ Digital Medicine
NPJ digital medicine, vol 3, iss 1
NPJ Digital Medicine
NPJ digital medicine, vol 3, iss 1
The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Inv
Autor:
Volkmar Falk, Titus Kühne, Felix Schoenrath, Nina Rank, Christof Stamm, Alexander Meyer, Boris Pfahringer, Carsten Eickhoff, Jörg Kempfert
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-12 (2020)
Rank, N.; Pfahringer, B.; Kempfert, J.; Stamm, C.; Kühne, T.; Schoenrath, F.; Falk, V.; Eickhoff, C.; Meyer, A.: Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. In: npj Digital Medicine . Vol. 3 (2020) 1, 139. (DOI: /10.1038/s41746-020-00346-8)
NPJ Digital Medicine
npj Digital Medicine, 3 (1)
Rank, N.; Pfahringer, B.; Kempfert, J.; Stamm, C.; Kühne, T.; Schoenrath, F.; Falk, V.; Eickhoff, C.; Meyer, A.: Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. In: npj Digital Medicine . Vol. 3 (2020) 1, 139. (DOI: /10.1038/s41746-020-00346-8)
NPJ Digital Medicine
npj Digital Medicine, 3 (1)
Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. One important reason is that the amount of postoperative data is too
Autor:
Meimei Dongye, Yu Han, Duoru Lin, Xiaohang Wu, Chuan Chen, Lanqin Zhao, Ping Zhang, Yi Zhu, Danyao Nie, Chong Guo, Fabao Xu, Zhongwen Li, Pisong Yan, Haotian Lin, Chenjin Jin
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-9 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have be
Autor:
Lei Zhong, Zhuoling Lin, Yi Xiang, Xiaohang Wu, Hui Chen, Zhe Dong, Xin Liu, Xiyang Liu, Jing Li, Gang Tan, Xiaoyan Li, Fan Xu, Wenbin Wei, Yizhi Liu, Kai Huang, Min Li, Chuan Chen, Xinhua Liu, Xulin Zhang, Jiewei Jiang, Jian Lv, Jingjing Chen, Daoyao Nie, Haotian Lin, Qishan Zheng, Duoru Lin, Erping Long, Yi Zhu, Zhenzhen Liu, Meimei Dongye, Shiqi Ling, Wangting Li, Bo Yun, Weirong Chen, Liming Wang, Yifan Xiang, Chong Guo, Lanqin Zhao, Junhong Chen, Li Zhang, Dongni Wang
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-10 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; ho
Autor:
Sarah T. Cherng, Riccardo Miotto, Benjamin S. Glicksberg, Cesare Furlanello, Hao-Chih Lee, Matteo Danieletto, Giulia Landi, Isotta Landi, Joel T. Dudley
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-11 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysi
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-14 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
Endometriosis is a systemic and chronic condition in women of childbearing age, yet a highly enigmatic disease with unresolved questions: there are no known biomarkers, nor established clinical stages. We here investigate the use of patient-generated
Autor:
Alissa R Groisser, David M. Levine, Jennifer S. Haas, Lisa P. Newmark, David W. Bates, Zoe Co, A Jay Holmgren
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-7 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
Mobile health applications (“apps”) have rapidly proliferated, yet their ability to improve outcomes for patients remains unclear. A validated tool that addresses apps’ potentially important dimensions has not been available to patients and cli
Autor:
Peter Feys, Ilse Lamers, Jeremia P. O. Held, Anne Schwarz, Andreas R. Luft, Cynthia Gagnon, Christoph M. Kanzler, Olivier Lambercy, Mike D. Rinderknecht, Roger Gassert
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-17 (2020)
npj Digital Medicine, 3 (1)
NPJ Digital Medicine
npj Digital Medicine, 3 (1)
NPJ Digital Medicine
Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract met
Autor:
Youbao Tang, Zhiyong Lu, Mei Han, Ronald M. Summers, Catherine Brandon, Ke Yan, Yifan Peng, Yuxing Tang, Mohammadhadi Bagheri, Jing Xiao, Bernadette Redd
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest
Autor:
Christina Felix, Aziz Nazha, Alex Milinovich, Chris Donovan, Andrew Proctor, Nirav Vakharia, C. Beau Hilton, Timothy Crone
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We condu