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Patients resuscitated from cardiac arrest who enter a coma are at high riskof death. Forecasting neurological outcomes of these patients (the task ofneurological prognostication) could help with treatment decisions. In thispaper, we propose, to the best of our knowledge, the first dynamic frameworkfor neurological prognostication of post-cardiac-arrest comatose patients usingEEG data: our framework makes predictions for a patient over time as more EEGdata become available, and different training patients' available EEG timeseries could vary in length. Predictions are phrased in terms of eithertime-to-event outcomes (time-to-awakening or time-to-death) or as the patient'sprobability of awakening or of dying across multiple time horizons. Ourframework uses any dynamic survival analysis model that supports competingrisks 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cumulative incidence functions.We consider three competing risks as to what happens first to a patient:awakening, being withdrawn from life-sustaining therapies (and thusdeterministically dying), or dying (by other causes). We demonstrate ourframework by benchmarking three existing dynamic survival analysis models thatsupport competing risks on a real dataset of 922 patients. Our mainexperimental findings are that: (1) the classical Fine and Gray model whichonly uses a patient's static features and summary statistics from the patient'slatest hour's worth of EEG data is highly competitive, achieving accuracyscores as high as the recently developed Dynamic-DeepHit model that usessubstantially more of the patient's EEG data; and (2) in an ablation study, weshow that our choice of modeling three competing risks results in a model thatis at least as accurate while learning more information than simpler models(using two competing risks or a standard survival analysis setup with nocompeting risks).

Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing Risks