Article: Current challenges in adopting machine learning to critical care and emergency medicine.
Clinical and experimental emergency medicine
2023 Volume 10, Issue 2, Page(s) 132–137
Abstract: Over the past decades, the field of machine learning (ML) has made great strides in medicine. Despite the number of ML-inspired publications in the clinical arena, the results and implications are not readily accepted at the bedside. Although ML is very ... ...
| Abstract | Over the past decades, the field of machine learning (ML) has made great strides in medicine. Despite the number of ML-inspired publications in the clinical arena, the results and implications are not readily accepted at the bedside. Although ML is very powerful in deciphering hidden patterns in complex critical care and emergency medicine data, various factors including data, feature generation, model design, performance assessment, and limited implementation could affect the utility of the research. In this short review, a series of current challenges of adopting ML models to clinical research will be discussed. |
|---|---|
| Language | English |
| Publishing date | 2023-05-15 |
| Publishing country | Korea (South) |
| Document type | Journal Article |
| ISSN | 2383-4625 |
| ISSN | 2383-4625 |
| DOI | 10.15441/ceem.23.041 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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