Acute Kidney Injury: Predicting 30-Day Readmissions

被引：0

作者：

Keyes, Michael C. ^{[1
]}

Bieniek, Joanna ^{[1
]}

Richey, Allison ^{[1
]}

Seetan, Raed ^{[1
]}

机构：

[1] Slippery Rock Univ, Dept Comp Sci, Slippery Rock, PA 16057 USA

来源：

2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2018年

关键词：

classification; health informatics; predictive modeling;

D O I：

10.1109/ICMLA.2018.00229

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many models have been developed to predict hospital 30-day readmissions. In this study, AUTO-WEKA was utilized against datasets with differing class imbalances to identify potential algorithms for further exploration. The study identified two algorithms, KStar and IBk, along with attribute selection criteria with high levels of sensitivity, specificity, precision, and accuracy. The two algorithms were identified with datasets containing higher class imbalances. Lower class imbalance datasets were not able to produce algorithms with acceptable performance.

引用

页码：1408 / 1412

页数：5

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