Machine learning for post-acute pancreatitis diabetes mellitus prediction and personalized treatment recommendations.
Machine learning for post-acute pancreatitis diabetes mellitus prediction and personalized treatment recommendations.
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DOI：
10.1038/s41598-023-31947-4
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发表时间：
2023-03-24
影响因子：
4.6
通讯作者：
Li, Ling
中科院分区：
文献类型：
--
作者：
Zhang, Jun;Lv, Yingqi;Hou, Jiaying;Zhang, Chi;Yua, Xuelu;Wang, Yifan;Yang, Ting;Su, Xianghui;Ye, Zheng;Li, Ling
Post-acute pancreatitis diabetes mellitus (PPDM-A) is the main component of pancreatic exocrine diabetes mellitus. Timely diagnosis of PPDM-A improves patient outcomes and the mitigation of burdens and costs. We aimed to determine risk factors prospectively and predictors of PPDM-A in China, focusing on giving personalized treatment recommendations. Here, we identify and evaluate the best set of predictors of PPDM-A prospectively using retrospective data from 820 patients with acute pancreatitis at four centers by machine learning approaches. We used the L1 regularized logistic regression model to diagnose early PPDM-A via nine clinical variables identified as the best predictors. The model performed well, obtaining the best AUC = 0.819 and F1 = 0.357 in the test set. We interpreted and personalized the model through nomograms and Shapley values. Our model can accurately predict the occurrence of PPDM-A based on just nine clinical pieces of information and allows for early intervention in potential PPDM-A patients through personalized analysis. Future retrospective and prospective studies with multicentre, large sample populations are needed to assess the actual clinical value of the model.
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影响因子：
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作者：
Schepers, Nicolien J.;Bakker, Olaf J.;Bruno, Marco J.
通讯作者：
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Petrov, Maxim S.