2024年6月3日发(作者：)
利用ChatGPT进行医学文本挖掘和预测分析（英文中文双
语版优质文档）
Medical Text Mining and Predictive Analysis Using ChatGPT
Medical text mining and predictive analysis is the use of
natural language processing and machine learning technology
to process and analyze large-scale text data in the medical
field, in order to extract valuable information, knowledge and
patterns, and perform prediction and decision support.
Research in this field aims to help doctors and researchers
better understand medical text data, discover potential
associations and regularities, and provide support and
guidance for medical decision-making and research.
Research on medical text mining and predictive analysis using
ChatGPT mainly includes the following aspects:
1. Text preprocessing: Medical text data usually has complex
structures and formats, such as medical records, clinical trial
reports, and medical literature. Before text mining, text data
needs to be preprocessed, including word segmentation, stop
word removal, stemming, etc. In addition, specific processing
and labeling are required for medical-specific problems, such
as entity labeling and relation extraction.
2. Entity recognition and annotation: Medical texts contain a
large amount of entity information, such as diseases, drugs,
symptoms, etc. By using the ChatGPT model, you can use its
powerful semantic understanding ability to perform entity
recognition and labeling on medical texts. This helps to extract
and count the frequency, co-occurrence relationship and
correlation of medical entities, providing support for medical
research and clinical decision-making.
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