Anesthesia research in the artificial intelligence era.
10.17085/apm.2018.13.3.248
- Author:
Hyung Chul LEE
1
;
Chul Woo JUNG
Author Information
1. Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea. spss@snuh.org
- Publication Type:Review
- Keywords:
Artificial intelligence;
Big data;
Machine learning;
Medical research
- MeSH:
Anesthesia*;
Artificial Intelligence*;
Decision Trees;
Humans;
Learning;
Machine Learning;
Medical Records;
Neural Networks (Computer);
Radiology Information Systems;
Support Vector Machine
- From:Anesthesia and Pain Medicine
2018;13(3):248-255
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
A noteworthy change in recent medical research is the rapid increase of research using big data obtained from electrical medical records (EMR), order communication systems (OCS), and picture archiving and communication systems (PACS). It is often difficult to apply traditional statistical techniques to research using big data because of the vastness of the data and complexity of the relationships. Therefore, the application of artificial intelligence (AI) techniques which can handle such problems is becoming popular. Classical machine learning techniques, such as k-means clustering, support vector machine, and decision tree are still efficient and useful for some research problems. The deep learning techniques, such as multi-layer perceptron, convolutional neural network, and recurrent neural network have been spotlighted by the success of deep belief networks and convolutional neural networks in solving various problems that are difficult to solve by conventional methods. The results of recent research using artificial intelligence techniques are comparable to human experts. This article introduces technologies that help researchers conduct medical research and understand previous literature in the era of AI.
