1 code implementation • ICLR 2021 • Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar
Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.
2 code implementations • 7 May 2019 • Hiske Overweg, Anna-Lena Popkes, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang
However, flexible tools such as artificial neural networks (ANNs) suffer from a lack of interpretability limiting their acceptability to clinicians.
1 code implementation • 8 Aug 2019 • Tom Edinburgh, Peter Smielewski, Marek Czosnyka, Stephen J. Eglen, Ari Ercole
Waveform physiological data is important in the treatment of critically ill patients in the intensive care unit.
1 code implementation • 8 Mar 2023 • Shubhayu Bhattacharyay, Pier Francesco Caruso, Cecilia Åkerlund, Lindsay Wilson, Robert D Stevens, David K Menon, Ewout W Steyerberg, David W Nelson, Ari Ercole, the CENTER-TBI investigators/participants
Here, we integrate all heterogenous data stored in medical records (1, 166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to six-month functional outcome on the Glasgow Outcome Scale - Extended (GOSE).
1 code implementation • 5 Mar 2020 • Jacob Deasy, Ari Ercole, Pietro Liò
In realistic scenarios, multivariate timeseries evolve over case-by-case time-scales.
1 code implementation • 23 Jul 2020 • James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
1 code implementation • 10 Feb 2022 • Shubhayu Bhattacharyay, Ioan Milosevic, Lindsay Wilson, David K. Menon, Robert D. Stevens, Ewout W. Steyerberg, David W. Nelson, Ari Ercole, the CENTER-TBI investigators/participants
We analysed the effect of 2 design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of 10 validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning.
no code implementations • 13 Sep 2019 • Jacob Deasy, Pietro Liò, Ari Ercole
Recordings in the first few hours of a patient's stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores within just 2 hours and achieving a state of the art Area Under the Receiver Operating Characteristic (AUROC) value of 0. 80 (95% CI 0. 79-0. 80) at 12 hours vs 0. 70 and 0. 66 for SAPS II and OASIS at 24 hours respectively.
no code implementations • 17 Sep 2019 • Jacob Deasy, Ari Ercole, Pietro Liò
Dynamic assessment of patient status (e. g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation.