Humans Versus Machine: Machine Learning‐Based Individualized Treatment Effect Modeling for Extracorporeal Cardiopulmonary Resuscitation in Out‐of‐Hospital Cardiac Arrest
Anna Condella, Nicholas J Johnson · 2026
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Description
Anna Condella and Nicholas J Johnson comment on machine learning for selecting patients for extracorporeal resuscitation, in a 2026 editorial. This rescue method can lift survival after refractory cardiac arrest, yet choosing candidates and running the logistics remain hard, and most scoring tools lack validation.
They discuss work by Chen and others in the same journal issue, covering 1,953 adults with out-of-hospital cardiac arrest across four Taiwanese centres over nine years. A causal forest model estimated each patient's survival gain with or without the treatment, moving past fixed entry rules. The editorial weighs that shift toward personal benefit estimates and the care needed before such models guide real-time decisions.
Catalog record
| CREATOR | Anna Condella, Nicholas J Johnson |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 8862 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |