Current challenges in adopting machine learning to critical care and emergency medicine
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 com...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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The Korean Society of Emergency Medicine
2023-05-01
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| Series: | Clinical and Experimental Emergency Medicine |
| Subjects: | |
| Online Access: | http://ceemjournal.org/upload/pdf/ceem-23-041.pdf |
| _version_ | 1797784047117664256 |
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| author | Cyra-Yoonsun Kang Joo Heung Yoon |
| author_facet | Cyra-Yoonsun Kang Joo Heung Yoon |
| author_sort | Cyra-Yoonsun Kang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T00:34:26Z |
| format | Article |
| id | doaj.art-fe3d4b58cacd4763a4231ad9615864a7 |
| institution | Directory Open Access Journal |
| issn | 2383-4625 |
| language | English |
| last_indexed | 2024-03-13T00:34:26Z |
| publishDate | 2023-05-01 |
| publisher | The Korean Society of Emergency Medicine |
| record_format | Article |
| series | Clinical and Experimental Emergency Medicine |
| spelling | doaj.art-fe3d4b58cacd4763a4231ad9615864a72023-07-10T07:52:12ZengThe Korean Society of Emergency MedicineClinical and Experimental Emergency Medicine2383-46252023-05-0110213213710.15441/ceem.23.041476Current challenges in adopting machine learning to critical care and emergency medicineCyra-Yoonsun Kang0Joo Heung Yoon1 Department of Internal Medicine, John H. Stroger Jr. Hospital of Cook County, Chicago, IL, USA Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USAOver 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.http://ceemjournal.org/upload/pdf/ceem-23-041.pdfmachine learningchallengesartificial intelligencecritical care |
| spellingShingle | Cyra-Yoonsun Kang Joo Heung Yoon Current challenges in adopting machine learning to critical care and emergency medicine Clinical and Experimental Emergency Medicine machine learning challenges artificial intelligence critical care |
| title | Current challenges in adopting machine learning to critical care and emergency medicine |
| title_full | Current challenges in adopting machine learning to critical care and emergency medicine |
| title_fullStr | Current challenges in adopting machine learning to critical care and emergency medicine |
| title_full_unstemmed | Current challenges in adopting machine learning to critical care and emergency medicine |
| title_short | Current challenges in adopting machine learning to critical care and emergency medicine |
| title_sort | current challenges in adopting machine learning to critical care and emergency medicine |
| topic | machine learning challenges artificial intelligence critical care |
| url | http://ceemjournal.org/upload/pdf/ceem-23-041.pdf |
| work_keys_str_mv | AT cyrayoonsunkang currentchallengesinadoptingmachinelearningtocriticalcareandemergencymedicine AT jooheungyoon currentchallengesinadoptingmachinelearningtocriticalcareandemergencymedicine |