A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910447858352128 |
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| author | Povinelli, Richard J Dupont, Mathew |
| author_facet | Povinelli, Richard J Dupont, Mathew |
| contents | Aim: Approximately six million people suffer cardiac arrests worldwide per year with very low survival rates (<1%). Thus, the aim of this study is to estimate the probability of a poor outcome after cardiac arrest. Accurate outcome predictions avoid removing care too soon for patients with potentially good outcomes or continuing care for patients with likely poor outcomes. Method: The method is based on dynamical systems embedding theorems that show that a reconstructed phase space (RPS) topologically equivalent to an underlying system can be constructed from measured signals. Here the underlying system is the human brain after a cardiac arrest, and the signals are the EEG channels. We model the RPS with a Gaussian mixture model (GMM) and ensemble the output of the RPS-GMM with clinical data via XGBoost. Results: As team Blue and Gold in the Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023, our RPS-GMM-XGBoost method obtained a test set competition score of 0.426 and rank of 24/36. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_08827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest Povinelli, Richard J Dupont, Mathew Quantitative Methods Dynamical Systems Aim: Approximately six million people suffer cardiac arrests worldwide per year with very low survival rates (<1%). Thus, the aim of this study is to estimate the probability of a poor outcome after cardiac arrest. Accurate outcome predictions avoid removing care too soon for patients with potentially good outcomes or continuing care for patients with likely poor outcomes. Method: The method is based on dynamical systems embedding theorems that show that a reconstructed phase space (RPS) topologically equivalent to an underlying system can be constructed from measured signals. Here the underlying system is the human brain after a cardiac arrest, and the signals are the EEG channels. We model the RPS with a Gaussian mixture model (GMM) and ensemble the output of the RPS-GMM with clinical data via XGBoost. Results: As team Blue and Gold in the Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023, our RPS-GMM-XGBoost method obtained a test set competition score of 0.426 and rank of 24/36. |
| title | A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest |
| topic | Quantitative Methods Dynamical Systems |
| url | https://arxiv.org/abs/2405.08827 |