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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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    科学领域:

    • 人工智能的人工智能
    • 临床信息学 临床信息学
    • 自然语言处理自然语言处理.

    背景情况:

    • 临床病例报告包含有价值的患者时间数据.
    • 传统的机器学习方法往往无法有效利用这些时间信息.
    • 大型语言模型 (LLM) 为分析非结构化临床文本提供了新的可能性.

    研究的目的:

    • 从临床笔记中引入并解决使用文字时间序列的预测问题.
    • 评估各种机器学习模型来预测患者的发展轨迹.
    • 为了比较不同模型架构在不同的时间预测任务上的性能.

    主要方法:

    • 使用LLM辅助注释管道提取时间标记的临床发现.
    • 从临床笔记开发了一个文字时间序列数据集.
    • 系统评估基于编码器的变压器和基于解码器的LLM在事件预测,时间排序和生存分析任务上.

    主要成果:

    • 基于编码器的模型在事件发生预测和时间排序任务 (更高的F1分数,更好的时间一致性) 中表现优于其他模型.
    • 微调的掩盖方法改善了排名表现.
    • 在生存分析中,指令调节的解码器模型显示了早期预后的优势.

    结论:

    • 临床文本的时间排序对于准确的时间预测至关重要,其性能优于简单的文本排序.
    • 可以有效地适应临床时间序列分析的LLM,增强患者轨迹预测.
    • 这项工作强调了时间顺序的临床数据在医疗保健中推进人工智能的价值.