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本文引用的文献

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Recurrent violent injury: magnitude, risk factors, and opportunities for intervention from a statewide analysis.复发性暴力伤害:基于全州分析的严重程度、风险因素及干预机会
Am J Emerg Med. 2016 Sep;34(9):1823-30. doi: 10.1016/j.ajem.2016.06.051. Epub 2016 Jun 15.
2
Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations.使用机器学习方法预测脑动静脉畸形放射外科治疗后的结果。
Sci Rep. 2016 Feb 9;6:21161. doi: 10.1038/srep21161.
3
The relationship between level of training and accuracy of violence risk assessment.训练水平与暴力风险评估准确性之间的关系。
Psychiatr Serv. 2012 Nov;63(11):1089-94. doi: 10.1176/appi.ps.201200019.
4
Fait accompli: suicide in a rural trauma setting.既成事实:农村创伤环境中的自杀行为。
J Trauma. 2009 Aug;67(2):366-71. doi: 10.1097/TA.0b013e3181ae81d5.
5
Self-injurious behaviors in a college population.大学生群体中的自伤行为。
Pediatrics. 2006 Jun;117(6):1939-48. doi: 10.1542/peds.2005-2543.

机器学习模型在穿透性创伤后再损伤预测中的应用。

Machine Learning Models for Prediction of Reinjury After Penetrating Trauma.

机构信息

Department of Surgery, University of Miami Leonard M. Miller School of Medicine, Miami, Florida.

出版信息

JAMA Surg. 2018 Feb 1;153(2):184-186. doi: 10.1001/jamasurg.2017.3116.

DOI:10.1001/jamasurg.2017.3116
PMID:29049476
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5838585/
Abstract

This study compares several machine learning models for prediction of reinjury after penetrating trauma.

摘要

本研究比较了几种用于预测穿透性创伤后再损伤的机器学习模型。