Feasibility and Effectiveness of a Low-Code AI Platform for Developing a Neonatal Multimodal Pain Classification Model.

IF 2.4 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Journal of Multidisciplinary Healthcare Pub Date : 2025-09-13 eCollection Date: 2025-01-01 DOI:10.2147/JMDH.S531709
Nannan Yang, Xiaosong Jiang, Xue Jin, Xinran Dai, Yuanjing Gu, Huiping Jiang, Liping Pu, Tingqi Shi
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引用次数: 0

Abstract

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.

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Abstract Image

Abstract Image

低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
背景:人工智能(AI)在新生儿疼痛识别方面取得了进展,但在将复杂算法转化为实际临床应用方面仍存在重大差距。低代码人工智能开发平台可以简化和自动化模型创建,为弥合研究和临床实践之间的差距提供了一个潜在的解决方案。目的:本研究旨在探讨利用商用低码人工智能开发平台(EasyDL)构建和验证新生儿多模态疼痛分类模型的可行性。目标是开发一种可访问的,具有成本效益的,高效的方法,使临床专业人员能够在没有广泛编程专业知识的情况下创建自己的人工智能工具。方法:将426个新生儿急性疼痛多模态数据片段上传到EasyDL平台,并利用其AutoML功能训练视频分类模型。该模型在保留数据集部分进行了内部测试,随后在独立的前瞻性队列中进行了外部验证。为了进行外部验证,我们将模型性能与N-PASS(新生儿疼痛、躁动和镇静量表)评分进行了比较,该评分由一位高级护士评估,作为临床金标准。结果:在该平台上建立的新生儿多模态疼痛分类模型表现良好。内部验证的准确率为89.6%,F1得分为85.8%。对未见数据的外部验证准确率达到87.7%,所有疼痛类别(无疼痛、轻度疼痛、重度疼痛)的AUC均超过0.95。简化的开发过程使API能够无缝部署到临床使用的Android移动设备。结论:利用低码人工智能平台建立新生儿多模态疼痛分类模型是可行且有效的。该模型表现出稳健的性能和强大的临床整合潜力。这种方法为人工智能开发的民主化提供了切实可行的途径,使医疗保健专业人员能够为新生儿疼痛管理创建数字解决方案。
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来源期刊
Journal of Multidisciplinary Healthcare
Journal of Multidisciplinary Healthcare Nursing-General Nursing
CiteScore
4.60
自引率
3.00%
发文量
287
审稿时长
16 weeks
期刊介绍: The Journal of Multidisciplinary Healthcare (JMDH) aims to represent and publish research in healthcare areas delivered by practitioners of different disciplines. This includes studies and reviews conducted by multidisciplinary teams as well as research which evaluates or reports the results or conduct of such teams or healthcare processes in general. The journal covers a very wide range of areas and we welcome submissions from practitioners at all levels and from all over the world. Good healthcare is not bounded by person, place or time and the journal aims to reflect this. The JMDH is published as an open-access journal to allow this wide range of practical, patient relevant research to be immediately available to practitioners who can access and use it immediately upon publication.
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