@Article{info:doi/10.2196/21043，作者=“Park, Ji Ae and Sung, Min Dong and Kim, Ho Heon and Park, Yu Rang”，标题=“基于权重的非迭代通信非数据共享多数据库预测建模框架:多机构研究的隐私保护分析方法”，期刊=“JMIR Med Inform”，年=“2021”，月=“4”，日=“5”，卷=“9”，号=“4”，页=“e21043”，关键词=“多机构研究”;分布式数据;数据共享;背景:在生物医学研究中，确保研究人群的代表性是确保高普遍性的关键。在这方面，使用多机构数据在医学上具有优势。然而，由于生物医学数据的机密性会导致隐私问题，因此很难对数据进行物理合并。因此，在使用多机构医疗数据进行研究时，有必要采用方法学方法来开发模型，而不需要在机构之间共享数据。目的:本研究旨在建立一个基于权重的多机构数据集成预测模型，该模型不需要机构间的迭代通信，通过提高模型在隐私保护条件下的泛化性，提高平均预测性能，而无需共享患者级数据。方法:基于权重的综合模型为每个机构模型生成一个权重，并基于这些权重构建多机构数据的综合模型。我们进行了3次模拟，以显示权重特性，并确定获得稳定值所需的权重重复次数。 We also conducted an experiment using real multi-institutional data to verify the developed weight-based integrated model. We selected 10 hospitals (2845 intensive care unit [ICU] stays in total) from the electronic intensive care unit Collaborative Research Database to predict ICU mortality with 11 features. To evaluate the validity of our model, compared with a centralized model, which was developed by combining all the data of 10 hospitals, we used proportional overlap (ie, 0.5 or less indicates a significant difference at a level of .05; and 2 indicates 2 CIs overlapping completely). Standard and firth logistic regression models were applied for the 2 simulations and the experiment. Results: The results of these simulations indicate that the weight of each institution is determined by 2 factors (ie, the data size of each institution and how well each institutional model fits into the overall institutional data) and that repeatedly generating 200 weights is necessary per institution. In the experiment, the estimated area under the receiver operating characteristic curve (AUC) and 95{\%} CIs were 81.36{\%} (79.37{\%}-83.36{\%}) and 81.95{\%} (80.03{\%}-83.87{\%}) in the centralized model and weight-based integrated model, respectively. The proportional overlap of the CIs for AUC in both the weight-based integrated model and the centralized model was approximately 1.70, and that of overlap of the 11 estimated odds ratios was over 1, except for 1 case. Conclusions: In the experiment where real multi-institutional data were used, our model showed similar results to the centralized model without iterative communication between institutions. In addition, our weight-based integrated model provided a weighted average model by integrating 10 models overfitted or underfitted, compared with the centralized model. The proposed weight-based integrated model is expected to provide an efficient distributed research approach as it increases the generalizability of the model and does not require iterative communication. ", issn="2291-9694", doi="10.2196/21043", url="https://medinform.www.mybigtv.com/2021/4/e21043", url="https://doi.org/10.2196/21043", url="http://www.ncbi.nlm.nih.gov/pubmed/33818396" }