Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.
Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.
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DOI：
10.1038/s41598-022-12497-7
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发表时间：
2022-05-19
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
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作者：
基于机器学习的临床预测模型从新机构的患者护理事件中学习的固有灵活性（特定于站点的培训）在应用于外部患者队列时会以性能下降为代价。为了充分利用跨机构临床大数据的潜力，机器学习系统必须能够跨机构边界转移知识，并从新的患者护理事件中学习，而不会忘记以前学习的模式。在这项工作中，我们开发了一种名为WUPERR（权重不确定性传播和情景再现）的隐私保护学习算法，并使用来自四个不同医疗系统的104，000多名患者的数据在早期预测脓毒症的背景下验证了该算法。我们测试了这一假设，即一旦在新的患者队列上进行了训练，所提出的持续学习算法就可以在以前的队列上保持比竞争方法更高的预测性能。在脓毒症预测任务中，在跨四个医院系统对深度学习模型进行增量训练后，（即医院H-A，H-B，H-C和H-D），与基线迁移学习方法相比，WUPERR在前三家医院中保持了最高的阳性预测值（H-A：39.27%对31.27%，H-B：25.34%对22.34%，H-C：30.33%对28.33%）。所提出的方法有可能构建更通用的模型，这些模型可以以隐私保护的方式从跨机构的临床大数据中学习。
The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct healthcare systems. We tested the hypothesis, that the proposed continual learning algorithm can maintain higher predictive performance than competing methods on previous cohorts once it has been trained on a new patient cohort. In the sepsis prediction task, after incremental training of a deep learning model across four hospital systems (namely hospitals H-A, H-B, H-C, and H-D), WUPERR maintained the highest positive predictive value across the first three hospitals compared to a baseline transfer learning approach (H-A: 39.27% vs. 31.27%, H-B: 25.34% vs. 22.34%, H-C: 30.33% vs. 28.33%). The proposed approach has the potential to construct more generalizable models that can learn from cross-institutional clinical big data in a privacy-preserving manner.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
19.7
作者：
Kim, Hyungjin;Goo, Jin Mo;Park, Chang Min
通讯作者：
Park, Chang Min
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
4.4
作者：
Abramoff, Michael David;Lou, Yiyue;Niemeijer, Meindert
通讯作者：
Niemeijer, Meindert
影响因子：
--
作者：
Abadi, Martin
通讯作者：
Abadi, Martin