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Current Trends in Nursing II

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

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Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

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神经象征性人工智能为妇女健康提供帮助

Mercedes Arguello¹, Julio Des Diz², Eric Jukes^1,3

¹BCS SGAI, UK.

Studies in health technology and informatics

|February 23, 2026

概括

此摘要是机器生成的。

这项研究探讨了一种神经象征性AI方法,将神经和象征性AI结合起来,以改善女性健康问题的可解释AI. 生物医学知识增强了从科学文献中提取和定制模式.

关键词:

大型语言模型神经符号AI是一种神经符号AI.存在学 (Ontologies) 是一种存在学.

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科学领域:

人工智能在医学中的应用
生物医学信息学生物医学信息学
计算语言学计算语言学

背景情况:

绝经和不孕症等妇女健康问题影响着全球人口的很大一部分.
庞大而不断增长的生物医学文献为当前的人工智能在提取可靠见解方面提出了挑战.
可操作的,机器可解释的疾病表征对于生物医学研究至关重要.

研究的目的:

调查本体学和知识图 (象征性AI) 可以在多大程度上支持人工神经网络 (神经AI) 的以人为中心的可解释AI.
探索一种神经象征性AI方法,将神经和象征性AI结合起来,用于处理生物医学文本.
评估将领域知识纳入人工智能模型的影响,以提高定制性和可解释性.

主要方法:

开发了一种神经象征性AI方法,将神经AI集成为模式提取与象征性AI用于背景知识表示.
来自生物医学文献的领域知识和科学证据被利用,以创建人类可读的解释 (可解释的AI) 作为纳米出版物 (知识图).
实验涉及将无监督向量算术公式 (cosine,3CosAdd) 应用于PubMed引用中的word2vec嵌入,并评估大语言模型 (LLM) 进行术语提取.

主要成果:

三个实验 (EXP1-EXP3) 评估了来自超过30万个PubMed引用的word2vec嵌入的315个n-gram.
第四个实验 (EXP4) 评估了9个LLM (包括专门的生物医学和一般模型) 从基于证据的文本中提取的381个术语.
该研究探讨了将先前的领域知识纳入向量算法以定制AI模型输出的实用性.

结论:

生物医学知识可以指导神经模型预测的可解释性,确定哪些预测需要解释,哪些可以忽视.
整合生物医学知识提高了人工智能模型的可定制性,特别是在使用word2vec嵌入和LLM用于健康问题模式提取时.
神经象征性方法为从生物医学文献中获得更可靠和更可解释的AI见解提供了一条途径.