TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine

Autor: Saveliev, Evgeny S., van der Schaar, Mihaela

TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component, focused on medicine and healthcare use cases. It supports data in time series, static, and eventmodalities and provides an interf

Externí odkaz: http://arxiv.org/abs/2301.12260

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge

Autor: Wang, Zichao, Lamb, Angus, Saveliev, Evgeny, Cameron, Pashmina, Zaykov, Yordan, Hernandez-Lobato, Jose Miguel, Turner, Richard E., Baraniuk, Richard G., Barton, Craig, Jones, Simon Peyton, Woodhead, Simon, Zhang, Cheng

This competition concerns educational diagnostic questions, which are pedagogically effective, multiple-choice questions (MCQs) whose distractors embody misconceptions. With a large and ever-increasing number of such questions, it becomes overwhelmin

Externí odkaz: http://arxiv.org/abs/2104.04034

Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge

Autor: Wang, Zichao, Lamb, Angus, Saveliev, Evgeny, Cameron, Pashmina, Zaykov, Yordan, Hernández-Lobato, José Miguel, Turner, Richard E., Baraniuk, Richard G., Barton, Craig, Jones, Simon Peyton, Woodhead, Simon, Zhang, Cheng

Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers tha

Externí odkaz: http://arxiv.org/abs/2007.12061

Hide-and-Seek Privacy Challenge:Synthetic Data Generation vs. Patient Re-identification

Autor: Jordon, James, Jarrett, Daniel, Saveliev, Evgeny, Yoon, Jinsung, Elbers, Paul, Thoral, Patrick, Ercole, Ari, Zhang, Cheng, Belgrave, Danielle, van der Schaar, Mihaela

Publikováno v: Jordon, J, Jarrett, D, Saveliev, E, Yoon, J, Elbers, P, Thoral, P, Ercole, A, Zhang, C, Belgrave, D & van der Schaar, M 2020, Hide-and-Seek Privacy Challenge : Synthetic Data Generation vs. Patient Re-identification . in H J Escalante & K Hofmann (eds), Proceedings of the NeurIPS 2020 Competition and Demonstration Track . vol. 133, Proceedings of Machine Learning Research, ML Research Press, pp. 206-215, 34th Demonstration and Competition Track at the 34th Annual Conference on Neural Information Processing Systems, NeurIPS 2020, Virtual, Online, 06/12/2020 .

The clinical time-series setting poses a unique combination of challenges to data modelling and sharing. Due to the high dimensionality of clinical time series, adequate de-identification to preserve privacy while retaining data utility is difficult

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_____10172::0f27206077fe5a963f56908084714c61
https://research.vumc.nl/en/publications/bd41cd9d-dacb-4bc6-95b1-4d0da5cbdffd

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer

Autor: Changhee Lee, Alexander Light, Evgeny S. Saveliev, Mihaela van der Schaar, Vincent J. Gnanapragasam

Publikováno v: NPJ digital medicine. 5(1)

Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has h

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55b188b475b9ffd6e7ddd562aea52f8e
https://pubmed.ncbi.nlm.nih.gov/35933478