Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using
EEG Data: A Dynamic Survival Analysis Framework with Competing Risks
- URL: http://arxiv.org/abs/2308.11645v2
- Date: Fri, 1 Dec 2023 03:32:25 GMT
- Title: Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using
EEG Data: A Dynamic Survival Analysis Framework with Competing Risks
- Authors: Xiaobin Shen, Jonathan Elmer, George H. Chen
- Abstract summary: We propose a framework for neurological prognostication of post-cardiac-arrest comatose patients using EEG data.
Our framework uses any dynamic survival analysis model that supports competing risks in the form of estimating patient-level cumulative incidence functions.
We demonstrate our framework by benchmarking three existing dynamic survival analysis models that support competing risks on a real dataset of 922 patients.
- Score: 4.487368901635044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Patients resuscitated from cardiac arrest who enter a coma are at high risk
of death. Forecasting neurological outcomes of these patients (the task of
neurological prognostication) could help with treatment decisions. In this
paper, we propose, to the best of our knowledge, the first dynamic framework
for neurological prognostication of post-cardiac-arrest comatose patients using
EEG data: our framework makes predictions for a patient over time as more EEG
data become available, and different training patients' available EEG time
series could vary in length. Predictions are phrased in terms of either
time-to-event outcomes (time-to-awakening or time-to-death) or as the patient's
probability of awakening or of dying across multiple time horizons. Our
framework uses any dynamic survival analysis model that supports competing
risks in the form of estimating patient-level cumulative incidence functions.
We consider three competing risks as to what happens first to a patient:
awakening, being withdrawn from life-sustaining therapies (and thus
deterministically dying), or dying (by other causes). We demonstrate our
framework by benchmarking three existing dynamic survival analysis models that
support competing risks on a real dataset of 922 patients. Our main
experimental findings are that: (1) the classical Fine and Gray model which
only uses a patient's static features and summary statistics from the patient's
latest hour's worth of EEG data is highly competitive, achieving accuracy
scores as high as the recently developed Dynamic-DeepHit model that uses
substantially more of the patient's EEG data; and (2) in an ablation study, we
show that our choice of modeling three competing risks results in a model that
is at least as accurate while learning more information than simpler models
(using two competing risks or a standard survival analysis setup with no
competing risks).
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