Discovering hidden information in biosignals from patients by artificial intelligence
Biosignals like electrocardiogram or photoplethysmogram have been widely used for monitoring and determining status of patients. However, it has been recently discovered that more information than that we have used traditionally were included in the biosignals after artificial intelligence (AI) was...
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| Vydáno v: | Korean journal of anesthesiology s. 275 - 284 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Korea (South)
대한마취통증의학회
01.08.2020
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| Témata: | |
| ISSN: | 2005-6419, 2005-7563 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Biosignals like electrocardiogram or photoplethysmogram have been widely used for monitoring and determining status of patients. However, it has been recently discovered that more information than that we have used traditionally were included in the biosignals after artificial intelligence (AI) was applied. Most meaningful advancement of current AI was in deep learning. The deep learning-based models show the best performance in most area in current due to the distinguished characteristic that it is able to extract important features from raw data. For that, deep learning extracts features in data by itself without feature engineering by human, if amount of data is enough for that. These AI-enabled feature give us opportunities to have a chance to see novel information which was hidden for many decades. It will be able to be used as digital biomarker for detecting or for predicting clinical outcome or event without further or more invasive evaluation. However, because the characteristics of deep learning is black box model, it is difficult to understand to use if users have the traditional view on the biosignals. For properly use of the novel information which is being discovered by AI and for adopting that in real clinical practice, clinicians need to basic knowledge on the AI and machine learning. This review covers from the basis of AI and machine learning for clinicians and its feasibilities in real practice within near future. |
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| ISSN: | 2005-6419 2005-7563 |
| DOI: | 10.4097/kja.19475 |