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English(EN) Scalable Clinical Data Infrastructure and Comparative ML Evaluation for Hospitalisation Risk Prediction in Elderly Patients with Multiple Long-Term Conditions using CPRD

评估用于预测老年人住院风险的机器学习模型

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文详细介绍了对患有多种长期疾病的老年患者预测住院风险的机器学习模型的全面评估。该研究使用CPRD Aurum数据开发了一个可扩展的临床数据基础设施，并将时间图卷积神经网络（TG-CNN）与LASSO正则化的逻辑回归和随机森林进行了基准测试。虽然TG-CNN在交叉验证中显示出略高的AUC-ROC，但LASSO正则化的逻辑回归在保留测试集上表现最佳，并显示出更优的校准，使其成为最适合直接临床部署的模型。 AI

影响 强调了校准和可解释性相对于原始判别力对于临床AI部署的重要性。

排序理由 发表在arXiv上的研究论文，详细介绍了机器学习模型评估。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

评估用于预测老年人住院风险的机器学习模型

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发表在arXiv上的研究论文，详细介绍了机器学习模型评估。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell, Aseel S. Abuzour, Michael Abaho, Danushka Bollegala, Gary Leeming, Eduard Shantsila, Andrew Clegg, Lauren E. Walker, Iain Edward Buchan, Samuel D. Relton ·

    使用CPRD对患有多种长期疾病的老年患者的住院风险预测进行可扩展的临床数据基础设施和比较机器学习评估

    arXiv:2608.29419v1 Announce Type: cross Abstract: Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over simpler, more interpretable models is rarely subjected to rigorous empirical scrut…

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