预测有30天非计划再次入院风险的患者。

Predicting Patients at Risk of 30-Day Unplanned Hospital Readmission.

作者信息

Baig Mirza, Hua Ning, Zhang Edmond, Robinson Reece, Armstrong Delwyn, Whittaker Robyn, Robinson Tom, Mirza Farhaan, Ullah Ehsan

机构信息

Data Science Team, Orion Health.

North Shore Hospital, Waitemata District Health Board.

出版信息

Stud Health Technol Inform. 2019 Aug 8;266:20-24. doi: 10.3233/SHTI190767.

DOI:10.3233/SHTI190767

PMID:31397296

Abstract

We developed a machine learning model to predict 30-day readmissions using the model types; XGBoost, Random Forests and Adaboost with decision stumps as a base learner with different feature combinations and preprocessing procedures. The proposed model achieved the F1-score (0.386 ± 0.006), sensitivity (0.598 ± 0.013), positive predictive value (PPV) (0.285 ± 0.004) and negative predictive value (NPV) (0.932 ± 0.002). When compared with LACE and PARR (NZ) models, the proposed model achieved better F1-score by 12.5% compared to LACE and 22.9% compared to PARR (NZ). The mean sensitivity of the proposed model was 6.0% higher than LACE and 42.4% higher than PARR (NZ). The mean PPV was 15.9% and 13.5% higher than LACE and PARR (NZ) respectively.

摘要

我们开发了一种机器学习模型，使用XGBoost、随机森林和以决策树桩作为基础学习器的Adaboost等模型类型，并结合不同的特征组合和预处理程序来预测30天再入院情况。所提出的模型实现了F1分数（0.386±0.006）、灵敏度（0.598±0.013）、阳性预测值（PPV）（0.285±0.004）和阴性预测值（NPV）（0.932±0.002）。与LACE和PARR（新西兰）模型相比，所提出的模型的F1分数比LACE高12.5%，比PARR（新西兰）高22.9%。所提出模型的平均灵敏度比LACE高6.0%，比PARR（新西兰）高42.4%。平均PPV分别比LACE和PARR（新西兰）高15.9%和13.5%。