Search Results for author: Ari Ercole

Found 7 papers, 4 papers with code

Clairvoyance: A Pipeline Toolkit for Medical Time Series

no code implementations 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.

AutoML Time Series

Adaptive Prediction Timing for Electronic Health Records

1 code implementation5 Mar 2020 Jacob Deasy, Ari Ercole, Pietro Liò

In realistic scenarios, multivariate timeseries evolve over case-by-case time-scales.

Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning

no code implementations17 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.

Variable Selection

Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing

no code implementations13 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.

Decision Making Feature Engineering +4

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care

2 code implementations7 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.

Decision Making Feature Selection +1

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