Search Results for author: Ari Ercole

Found 9 papers, 7 papers with code

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

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 +5

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

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.

The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach

1 code implementation10 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.

Decision Making

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury

1 code implementation8 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).

Data Integration Time Series

Clairvoyance: A Pipeline Toolkit for Medical Time Series

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.

AutoML Time Series

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