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已发表论文
低代码人工智能平台开发新生儿多模态疼痛分类模型的可行性和有效性
Authors Yang N , Jiang X , Jin X, Dai X, Gu Y, Jiang H, Pu L , Shi T
Received 1 April 2025
Accepted for publication 28 August 2025
Published 13 September 2025 Volume 2025:18 Pages 5771—5780
DOI https://doi.org/10.2147/JMDH.S531709
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Scott Fraser
Nannan Yang,1,* Xiaosong Jiang,2,* Xue Jin,2 Xinran Dai,2 Yuanjing Gu,3 Huiping Jiang,4 Liping Pu,5 Tingqi Shi1,6
1Department of Nursing, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China; 2School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China; 3Department of Emergency, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, People’s Republic of China; 4Department of Nursing, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, People’s Republic of China; 5School of Health Management, Suzhou Vocational Health College, Suzhou, Jiangsu, People’s Republic of China; 6Quality Management Department, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Tingqi Shi, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Gulou District, Nanjing, Jiangsu, People’s Republic of China, Email 13912996998@163.com Liping Pu, School of Health Management, Suzhou Vocational Health College, 20 Shuyuan Lane, Gusu District, Suzhou, Jiangsu, People’s Republic of China, Email jsszplp@163.com
Background: Artificial intelligence (AI) has advanced neonatal pain recognition, yet a significant gap persists in translating complex algorithms into practical clinical applications. Low-code AI development platforms, which simplify and automate model creation, offer a potential solution to bridge this gap between research and bedside practice.
Objective: This study aimed to explore the feasibility of constructing and validating a neonatal multimodal pain classification model using a commercial low-code AI development platform (EasyDL). The objective was to develop an accessible, cost-effective, and efficient method that empowers clinical professionals to create their own AI tools without extensive programming expertise.
Methods: We uploaded 426 neonatal acute pain multimodal data segments to the EasyDL platform and trained a video classification model using its AutoML capabilities. The model underwent internal testing on a held-out dataset portion, followed by external validation on an independent prospective cohort. For external validation, we compared model performance against the N-PASS (Neonatal Pain, Agitation, and Sedation Scale) scores assessed by a senior nurse as the clinical gold standard.
Results: The neonatal multimodal pain classification model developed on the platform showed strong performance. Internal validation achieved 89.6% accuracy and an 85.8% F1 score. External validation on unseen data reached 87.7% accuracy, with AUC exceeding 0.95 across all pain categories (no pain, mild pain, severe pain). The streamlined development process enabled seamless API deployment to an Android mobile device for clinical use.
Conclusion: Developing a neonatal multimodal pain classification model using a low-code AI platform proves both feasible and effective. The model demonstrates robust performance and strong clinical integration potential. This approach offers a practical pathway to democratize AI development, enabling healthcare professionals to create digital solutions for neonatal pain management.
Keywords: low-code platform, EasyDL, automated machine learning, AutoML, multimodal pain recognition, neonatal pain
1Department of Nursing, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China; 2School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China; 3Department of Emergency, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, People’s Republic of China; 4Department of Nursing, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, People’s Republic of China; 5School of Health Management, Suzhou Vocational Health College, Suzhou, Jiangsu, People’s Republic of China; 6Quality Management Department, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Tingqi Shi, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Gulou District, Nanjing, Jiangsu, People’s Republic of China, Email 13912996998@163.com Liping Pu, School of Health Management, Suzhou Vocational Health College, 20 Shuyuan Lane, Gusu District, Suzhou, Jiangsu, People’s Republic of China, Email jsszplp@163.com
Background: Artificial intelligence (AI) has advanced neonatal pain recognition, yet a significant gap persists in translating complex algorithms into practical clinical applications. Low-code AI development platforms, which simplify and automate model creation, offer a potential solution to bridge this gap between research and bedside practice.
Objective: This study aimed to explore the feasibility of constructing and validating a neonatal multimodal pain classification model using a commercial low-code AI development platform (EasyDL). The objective was to develop an accessible, cost-effective, and efficient method that empowers clinical professionals to create their own AI tools without extensive programming expertise.
Methods: We uploaded 426 neonatal acute pain multimodal data segments to the EasyDL platform and trained a video classification model using its AutoML capabilities. The model underwent internal testing on a held-out dataset portion, followed by external validation on an independent prospective cohort. For external validation, we compared model performance against the N-PASS (Neonatal Pain, Agitation, and Sedation Scale) scores assessed by a senior nurse as the clinical gold standard.
Results: The neonatal multimodal pain classification model developed on the platform showed strong performance. Internal validation achieved 89.6% accuracy and an 85.8% F1 score. External validation on unseen data reached 87.7% accuracy, with AUC exceeding 0.95 across all pain categories (no pain, mild pain, severe pain). The streamlined development process enabled seamless API deployment to an Android mobile device for clinical use.
Conclusion: Developing a neonatal multimodal pain classification model using a low-code AI platform proves both feasible and effective. The model demonstrates robust performance and strong clinical integration potential. This approach offers a practical pathway to democratize AI development, enabling healthcare professionals to create digital solutions for neonatal pain management.
Keywords: low-code platform, EasyDL, automated machine learning, AutoML, multimodal pain recognition, neonatal pain