[1]张娅峰,龚 振.基于迭代决策树的 ICU 临床干预预测[J].计算机技术与发展,2020,30(12):118-122.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 021]
　ZHANG Ya-feng,GONG Zhen.ICU Clinical Intervention Prediction Based on GBDT[J].,2020,30(12):118-122.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 021]
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基于迭代决策树的 ICU 临床干预预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年12期
页码:
118-122
栏目:
应用开发研究
出版日期:
2020-12-10

文章信息/Info

Title:
ICU Clinical Intervention Prediction Based on GBDT
文章编号:
1673-629X(2020)12-0118-05
作者:
张娅峰; 龚 振
华南理工大学,广东 广州 510641
Author(s):
ZHANG Ya-feng; GONG Zhen
South China University of Technology,Guangzhou 510641,China
关键词:
机器学习; 重症监护室; 干预; 预测; 迭代决策树
Keywords:
machine learning; intensive care unit; intervention; prediction; gradient boosting decision tree
分类号:
TP181
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 12. 021
摘要:
在重症监护室中,临床干预的实时预测仍然是一个挑战。 由于近来数字化趋势的发展,医院记录的信息越来越多。 医生可以访问有关患者的大量数据,但能用来处理数据的时间和工具很少。 智能的临床决策支持可以为医生提供患者何时需要特定干预的预测信息。 面对 ICU 病房数据密度大、质量高的特点, 善于处理海量数据的机器学习算法吸引了医疗界的关注。 通过提取患者在 ICU 期间产生的动态时间序列数据及患者静态人口学数据进行整合,使用 GBDT、SVM 等机器学习算法开发模型, 学习这些数据的表现以预测患者何时需要进行连续肾脏治疗干预。预测是以一种前瞻性的方式进行的,以实现“实时”的性能。 实验表明 GBDT 模型的准确率和召回率均达到 80% 以上。基于 GBDT 的 ICU 临床干预预测模型能够辅助临床医生进行风险预警,及时采取干预措施从而改善患者预后。
Abstract:
In the intensive care unit,real-time prediction of clinical interventions remains a challenge. As a result of the recent trend towards digitization,a growing amount of information is recorded in hospitals. Doctors have access to a wealth of data about patients,but they have little time and tools to process the data. Intelligent clinical decision support can provide doctors with predictive information about when patients need specific interventions. Facing the characteristics of high density and high quality of data in ICU wards,machine learning algorithms that are good at processing massive data have attracted the attention of the medical profession. By extracting the dynamic time series data and static demographic data generated by the patient during the ICU,the machine learning algorithms such as GBDT and SVM are used to develop models to learn the representation of these data to predict when the patient needs continuous renal therapy intervention. Forecasting is done in a forward-looking way to achieve “real-time” performance. Experiment shows that the precision and recall of the GBDT model are both above 80%. The GBDT-based ICU clinical intervention prediction model can assist clinicians in early warning of risks and timely intervention measures to improve patient prognosis.

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更新日期/Last Update: 2020-12-10