2 citations · 2 across the 3 of their papers we have counts for
5 papers
DynSHAP: Towards Explainable Dynamic Survival Analysis
Nastasya Anokhina, Jonas Jürß, Pietro Liò
Deep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their black box nature limits clinica…
KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis
Stelios Boulitsakis Logothetis, Angela Wood, Pietro Liò
Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as the Cox model and generalised…
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…
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…
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,…