3 papers
cs.LG2020
Adaptive Prediction Timing for Electronic Health Records
Jacob Deasy, Ari Ercole, Pietro Liò
In realistic scenarios, multivariate timeseries evolve over case-by-case time-scales. This is particularly clear in medicine, where the rate of clinical events varies by ward, pati…
cs.LG2019
Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning
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 alerti…
cs.LG2019
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing
Jacob Deasy, Pietro Liò, Ari Ercole
We present a machine learning pipeline and model that uses the entire uncurated EHR for prediction of in-hospital mortality at arbitrary time intervals, using all available chart,…