Author: Zen and the Art of Computer Programming
1 Introduction
In this new AI era, the healthcare industry is facing huge opportunities for change. Emerging technologies such as big data, cloud computing, artificial intelligence, and machine learning are driving the transformation and upgrading of the healthcare industry, causing earth-shaking changes in the entire industry. How to achieve technological innovation and business iteration in the healthcare industry, become an industry leader and achieve success, has become a highlight in the healthcare industry and one of the focuses of the industry.
2019 is the first year of ensemble learning models, a cutting-edge discipline that can solve multi-task and multi-modal problems. From medical image segmentation to brain image recognition, from intelligent diagnosis to precise treatment, highly automated and intelligent solutions are required. Among them, the application of integrated learning models in the real world has gradually become a hot topic.
In addition, as the complexity of the medical care field increases, traditional single models are unable to handle complex medical problems. Integrated learning models based on data mining and machine learning can provide more advanced functions. Because ensemble learning models have good classification performance and robustness, their application scope continues to expand. For example, in the field of imaging diagnosis, using medical imaging data sets to build a variety of classifier combinations can effectively improve the diagnosis effect; in the field of precision medicine, by building an auxiliary diagnosis model based on patient genetic data, it can help doctors better Identify rare diseases in patients and treat them promptly; in the field of medicine and pharmaceuticals, by using multiple signal sources such as bioinformatics and large-scale Internet data, combined with multiple learning methods to establish predictive models, it can help pharmaceutical companies develop more accurately and quickly drug.
With the theme of "Model Application", this article elaborates on the application of integrated learning models in the healthcare industry and various typical scenarios. I hope readers can read and understand it from the following aspects.
1. Background introduction
The "Model Application" section focuses on the application scenarios of integrated learning models in the healthcare industry, including:
(1) Image classification
The analysis process of medical imaging images involves multiple modal data&#x