人工智能,精神疾病和各种数据模式的相互作用:基于"方法-疾病-数据"轴的潜在治疗管理
Xu Tian1,2, Ning Wang2,3, Jin Yan4
1Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Toxicology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, China.
人工智能 (AI) 正在通过整合各种数据和方法来实现早期检测,诊断和个性化治疗来彻底改变心理健康. 这项研究绘制了神经精神病学中的人工智能应用,解决了未来临床整合的挑战.
科学领域:
- 神经精神病学
- 人工智能
- 数据科学
背景情况:
- 之前的研究单独分析了人工智能对心理健康的影响.
- 人工智能技术方法,数据集和应用的整体整合缺乏.
研究的目的:
- 在心理健康研究中系统地提取和整合人工智能信息.
- 开发一个知识图,描绘人工智能方法,精神障碍和数据模式.
- 突出人工智能与神经精神病学的关键研究领域和应用.
主要方法:
- 使用BioBERT预训练的语言模型来提取信息.
- 开发了一个广泛的知识图表,包含3158个实体和3248个关系.
- 使用"方法-疾病-数据"分析框架.
主要成果:
- 知识图映射了人工智能框架,将方法,障碍和数据类型联系起来.
- 确定了早期检测,诊断准确性和个性化治疗的关键应用.
- 突出了人工智能在识别大脑区域和神经回路方面的作用.
结论:
- 人工智能为推进心理健康诊断和治疗提供了重要机会.
- 挑战包括数据不平衡,伦理问题和临床可靠性.
- 提出了人工智能驱动精神病治疗的战略和未来前景.
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