Application of Machine Learning and Data Mining in Medicine: Opportunities and Considerations

Abstract

With the continuous development of information technology, machine learning and data mining have gradually found widespread applications across various industries. These technologies delve deeper into uncovering intrinsic patterns through the application of computer science. This trend is especially evident in today’s era of advanced artificial intelligence, which marks the anticipated third industrial revolution. By harnessing cutting-edge techniques such as multimodal large-scale models, artificial intelligence is profoundly impacting traditional scientific research methods. The use of machine learning and data mining techniques in medical research has a long-standing history. In addition to traditional methods such as logistic regression, decision trees, and Bayesian analysis, newer technologies such as neural networks, random forests, support vector machines, Histogram-based Gradient Boosting, XGBoost, LightGBM, and CatBoost have gradually gained widespread adoption. Each of these techniques has its own advantages and disadvantages, requiring careful selection based on the specific research objectives in clinical practice. Today, with the emergence of large language models such as ChatGPT 3.5, machine learning and data mining are gaining new meanings and application prospects. ChatGPT offers benefits such as optimized code algorithms and ease of use, saving time and enhancing efficiency for medical researchers. It is worth promoting the use of ChatGPT in clinical research.

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