Firearm injury risk detection and prevention.
作者信息
Bakken Suzanne
机构信息
Department of Biomedical Informatics, School of Nursing, Data Science Institute, Columbia University, New York, NY 10032, United States.
出版信息
J Am Med Inform Assoc. 2024 Oct 1;31(10):2163-2164. doi: 10.1093/jamia/ocae245.
Abstract
摘要
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本文引用的文献
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A machine-learning prediction model to identify risk of firearm injury using electronic health records data.利用电子健康记录数据建立机器学习预测模型识别枪支伤害风险
J Am Med Inform Assoc. 2024 Oct 1;31(10):2173-2180. doi: 10.1093/jamia/ocae222.
2
Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020.机器学习对圣路易斯地区新枪伤事件的分类：2010-2020 年。
J Am Med Inform Assoc. 2024 Oct 1;31(10):2165-2172. doi: 10.1093/jamia/ocae173.
3
Nutrition facts, drug facts, and model facts: putting AI ethics into practice in gun violence research.营养事实、药物事实和模型事实：将人工智能伦理应用于枪支暴力研究。
J Am Med Inform Assoc. 2024 Oct 1;31(10):2414-2421. doi: 10.1093/jamia/ocae102.
4
Measuring interpersonal firearm violence: natural language processing methods to address limitations in criminal charge data.测量人际枪支暴力：解决刑事指控数据局限性的自然语言处理方法。
J Am Med Inform Assoc. 2024 Oct 1;31(10):2374-2378. doi: 10.1093/jamia/ocae082.
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Firearm Storage in US Households With Children: Findings From the 2021 National Firearm Survey.美国家庭中存放枪支的情况：2021 年全国枪支调查的结果。
JAMA Netw Open. 2022 Feb 1;5(2):e2148823. doi: 10.1001/jamanetworkopen.2021.48823.
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Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.透明报告个体预后或诊断的多变量预测模型（TRIPOD）：TRIPOD 声明。
Ann Intern Med. 2015 Jan 6;162(1):55-63. doi: 10.7326/M14-0697.
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