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These unstructured text data create serious challenges for biomedical researchers to find information. Biomedical named entity recognition (BioNER) and biomedical relation extraction (BioRE) are the two most fundamental tasks of biomedical text mining. Accurately and efficiently identifying entities and extracting relations have become very important. Methods that perform two tasks separately are called pipeline models, and they have shortcomings such as insufficient interaction, low extraction quality and easy redundancy. To overcome the above shortcomings, many deep learning-based joint name entity recognition and relation extraction models have been proposed, and they have achieved advanced performance. This paper comprehensively summarize deep learning models for joint name entity recognition and relation extraction for biomedicine. The joint BioNER and BioRE models are discussed in the light of the challenges existing in the BioNER and BioRE tasks. Five joint BioNER and BioRE models and one pipeline model are selected for comparative experiments on four biomedical public datasets, and the experimental results are analyzed. Finally, we discuss the opportunities for future development of deep learning-based joint BioNER and BioRE models.<\/jats:p>","DOI":"10.1093\/bib\/bbac342","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T14:02:11Z","timestamp":1663682531000},"source":"Crossref","is-referenced-by-count":9,"title":["Deep learning joint models for extracting entities and relations in biomedical: a survey and comparison"],"prefix":"10.1093","volume":"23","author":[{"given":"Yansen","family":"Su","sequence":"first","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone , 230601, Hefei, China"}]},{"given":"Minglu","family":"Wang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone , 230601, Hefei, China"}]},{"given":"Pengpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone , 230601, Hefei, China"}]},{"given":"Chunhou","family":"Zheng","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone , 230601, Hefei, China"}]},{"given":"Yuansheng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan University , 2 Lushan S Rd, Yuelu District, 410086, Changsha, China"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan University , 2 Lushan S Rd, Yuelu District, 410086, Changsha, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,19]]},"reference":[{"issue":"1","key":"2022112110592096300_ref1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/s41591-019-0727-5","article-title":"Axes of a revolution: challenges and promises of big data in healthcare","volume":"26","author":"Shilo","year":"2020","journal-title":"Nat Med"},{"issue":"5","key":"2022112110592096300_ref2","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.molcel.2006.02.012","article-title":"Biomedical language processing: perspective what\u2019s beyond PubMed?","volume":"21","author":"Hunter","year":"2006","journal-title":"Mol 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