Research Article: Research Hotspots and Trends of Deep Learning in Critical Care Medicine: A Bibliometric and Visualized Study

Abstract:
Deep learning (DL) is a subset of machine learning (ML) that is created using complex algorithms that are inspired by the organization of the human brain with many discrete nodes or ‘neurons’ and can identify important patterns or features in a dataset. DL and ML refer to two different technologies, and DL is considered an advanced structure of ML. Convolutional neural networks, long and short-term memory networks, recurrent neural networks, transformer models, and attention mechanisms are all common u DL technologies. ML techniques are a collection of mathematical and statistical concepts such as support vector machine, random forest, and K-nearest neighbors. Whereas DL algorithms are specialized techniques that are a subset of ML. The most important difference between the two approaches is that ML requires a feature engineering process that eliminates unnecessary variables and pre-selects only those that will be used for learning. This process is disadvantaged by the requirement that experienced professionals pre-select critical variables. Conversely, DL algorithms overcomes this shortfall by a process in which have built-in mechanisms for assessing and addressing the root of any inaccuracies and do not require guidance. To “interpret” an image, for example, to deconstruct the image into specific features, such as brightness, curvature, sharpness, etc., the extraction process of the support vector machine, a ML technique, requires digital input into the computer algorithms to extract these image features. Whereas this feature extraction process is completely different in DL algorithms. By varying the weights of the given features, DL applies a series of convolutional filters to the image, and the DL algorithm can then be trained to recognize a specific type of image and ultimately achieve the extraction of features from the image.

Introduction:
Deep learning (DL) is a subset of machine learning (ML) that is created using complex algorithms that are inspired by the organization of the human brain with many discrete nodes or ‘neurons’ and can identify important patterns or features in a dataset. DL and ML refer to two different technologies, and DL is considered an advanced structure of ML. Convolutional neural networks, long and short-term memory networks, recurrent neural networks, transformer models, and attention mechanisms are all common u DL…

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