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| 基于机器学习的术后谵妄预测模型研究进展 |
| Research progress in machine learning-based prediction model of postoperative delirium |
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| DOI：10.12089/jca.2026.06.012 |
| 中文关键词: 机器学习 术后谵妄 多模态数据 预测模型 |
| 英文关键词: Machine learning Postoperative delirium Multimodal data Prediction model |
| 基金项目:中央高校基本科研业务费专项资金资助项目（YG2024LC10）；东方英才计划拔尖人才项目（BJKJ2024039） |
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| 中文摘要: |
| 术后谵妄（POD）是围术期常见的严重并发症，影响患者预后。传统风险评估模型在预测精度和个体化方面存在局限，因此开始出现使用人工智能进行谵妄预测的研究。近年来，随着医疗数据类型的不断丰富，机器学习（ML）作为一种强大的数据驱动技术，在POD预测研究中展现出巨大潜力。本文分别梳理基于不同数据模态的ML预测研究进展，并系统讨论该领域面临的关键挑战与未来方向。 |
| 英文摘要: |
| Postoperative delirium (POD) is a common and serious perioperative complication that significantly impacts patient prognosis. Traditional risk assessment models have limitations in predictive accuracy and personalization, leading to research into using artificial intelligence for POD prediction. In recent years, with the increasing diversity of medical data types, machine learning (ML), as a powerful data-driven technology, has demonstrated great potential in POD prediction research. This article will review the progress of ML prediction research based on different data modalities and systematically discuss the key challenges and future directions in this field. |
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