The anesthesiologist’s guide to critically assessing machine learning research: a narrative review
Felipe Ocampo Osorio, Sergio Alzate-Ricaurte, Tomas Eduardo Mejia Vallecilla, Gustavo Adolfo Cruz-Suarez · 2024
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Description
Felipe Ocampo Osorio and co-authors write a guide for anesthesiologists judging machine learning studies. Their twenty twenty-four narrative review notes that AI tools now predict kidney injury, tune anesthetic dose, and manage nausea after surgery. Daily practice gains precision, but only if the models are sound.
The guide walks through evaluation metrics that grade a model in plain stats. It also covers tools like SHAP values, which show how each input sways a forecast. Readers learn checks for validity, safety, and fit to real clinic work. The aim is doctors who can spot a flashy but hollow model before it reaches patients.
Catalog record
| CREATOR | Felipe Ocampo Osorio, Sergio Alzate-Ricaurte, Tomas Eduardo Mejia Vallecilla, Gustavo Adolfo Cruz-Suarez |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 37 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |