Intelligent analysis of laboratory equipment testing data based on BERT
-
Yanbin Zhang
,
Limin Du
Abstract
Review of laboratory test data is a management focus for compliance laboratories. Using BERT-based intelligent analysis to analyze the laboratory instrument test data can assist laboratory testing personnel to quickly and accurately obtain the target data. This paper, with high-performance liquid chromatography (HPLC) as an example, applies big data technology and machine learning algorithm to the intelligent analysis and application of instrument test data by reading the output files in combination with the historical data of laboratory management system and business system, to realize the automatic acquisition of instrument test data, which reduces the laboratory labor cost, improves the laboratory test efficiency, optimizes the laboratory test management, and improves the quality control level.
-
Research ethics: This article does not contain any studies with human participants performed by any of the authors.
-
Author contributions: Yanbin Zhang, Dan Liu, Yang Su is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. Limin Du, Xiaoyu Ge, Xinsheng Gong, and Jinshun Zhou is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: Authors do not have any conflicts.
-
Research funding: Authors did not receive any funding.
-
Data availability: No datasets were generated or analyzed during the current study.
References
1. Zhang, L. Analysis of the problems and improvement measures in the management of laboratory instruments and equipment in testing institutions. Mass Stand 2021;17:225–7.Search in Google Scholar
2. Ren, C. Research on new methods of quality control in testing laboratory. Qual Explor 2016;1:72–3.Search in Google Scholar
3. Li, D. Instrumentation deep knowledge services based on cloud framework [dissertation]. Jilin University; 2015. Available from: https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2015&filename=1015590955.nh.Search in Google Scholar
4. Wang, XP, Zuo, Y. Application and prospect of food testing instruments and equipment in food testing. China Food Saf Mag 2021;36:48–50.Search in Google Scholar
5. IBM. CRISP-DM help overview [Internet]. Available from: https://www.ibm.com/docs/en/spss-modeler/saas?topic=dm-crisp-help-overview.Search in Google Scholar
6. Chen, C. Construction of smart library based on the big data mining and knowledge discovery. J Mod Inf 2017;8:85–91.Search in Google Scholar
7. PdfMiner docs [Internet]. Available from: https://pdfminer-docs.readthedocs.io/pdfminer_index.html.Search in Google Scholar
8. Lample, G, Ballesteros, M, Subramanian, S, Kawakami, K, Dyer, C. Neural architectures for named entity recognition. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, CA: Association for Computational Linguistics; 2016. p. 260–70.10.18653/v1/N16-1030Search in Google Scholar
9. Hammerton, J. Named entity recognition with long short-term memory. In: Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL. Stroudsburg, PA, USA; 2003, vol 4. 172–5 pp.10.3115/1119176.1119202Search in Google Scholar
10. Xie, T, Yang, J, Liu, H. Chinese entity recognition based on BERT-BiLSTM-CRF model. Comput Syst Appl 2020;29:48–55.Search in Google Scholar
11. Devlin, J, Chang, M, Lee, K, Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, Association for Computational Linguistics: Minneapolis, Minnesota, MN, 2019, vol 1 (Long and Short Papers), 4171–86 pp.Search in Google Scholar
12. Huang, Z, Xu, W, Yu, K. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991; 2015. Available from: https://doi.org/10.48550/arXiv.1508.01991.Search in Google Scholar
13. Hochreiter, S, Schmidhuber, J. Long short-term memory. Neural Comput 1997;9:1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.Search in Google Scholar PubMed
14. He, Q, Zhao, L, Kuang, G. SAR airport runway extraction method based on semantic segmentation model and conditional random field. Mod Radar 2021;43:91–100.Search in Google Scholar
15. Nacha, C, Jing, S, Ian, W. Formal security analysis for software architecture design: an expressive framework to emerging architectural styles. Sci Comput Program 2021;206:102–10.10.1016/j.scico.2021.102631Search in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
