Advancements in predicting and modeling rare event outcomes for enhanced decision-making

被引：5

作者：

Feng, Cindy ^{[1
,2
]}

Li, Longhai ^{[3
]}

Xu, Chang ^{[4
,5
]}

机构：

[1] Dalhousie Univ, Fac Med, Dept Community Hlth & Epidemiol, 5790 Univ Ave, Halifax, NS B3H 1V7, Canada

[2] Univ Saskatchewan, Sch Publ Hlth, 104 Clin Pl, Saskatoon, SK S7N2Z4, Canada

[3] Univ Saskatchewan, Dept Math & Stat, 106 Wiggins Rd, Saskatoon, SK S7N5E6, Canada

[4] Anhui Med Univ, Key Lab Populat Hlth Across Life Cycle, Minist Educ, Hefei, Anhui, Peoples R China

[5] Anhui Med Univ, Sch Publ Hlth, Hefei, Anhui, Peoples R China

来源：

BMC MEDICAL RESEARCH METHODOLOGY | 2023年 / 23卷 / 01期

关键词：

LOGISTIC-REGRESSION; PROPENSITY SCORE; NUMBER;

D O I：

10.1186/s12874-023-02060-x

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showcased in this issue aims to provide valuable insights and strategies to enhance the accuracy of rare event prediction and modeling.

引用

页数：3

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