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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| Veröffentlicht in: | Korean journal of anesthesiology S. 275 - 284 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Korea (South)
대한마취통증의학회
01.08.2020
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| ISSN: | 2005-6419, 2005-7563 |
| Online-Zugang: | Volltext |
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| Abstract | 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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| AbstractList | 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. Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future. KCI Citation Count: 0 |
| Author | Yoon, Dukyong Kim, Tae Young Jang, Jong-Hwan Han, Chang Ho Choi, Byung Jin |
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| Keywords | Electrocardiography Electrodiagnosis Biomarker Artificial intelligence Computer-Assisted Diagnosis Deep Learning |
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| Snippet | Biosignals like electrocardiogram or photoplethysmogram have been widely used for monitoring and determining status of patients. However, it has been recently... Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently... |
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| Title | Discovering hidden information in biosignals from patients by artificial intelligence |
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