Regarding: Shung et al: Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.
作者信息
Kim Hyun-Seok, Peng Frederick B, Gomez Cifuentes Juan David
机构信息
Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas.
出版信息
Gastroenterology. 2020 Jun;158(8):2308-2309. doi: 10.1053/j.gastro.2020.01.055. Epub 2020 Mar 19.
Abstract
摘要
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Regarding: Shung et al: Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.关于：Shung等人：一种优于上消化道出血临床风险评分系统的机器学习模型的验证
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本文引用的文献
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Application of Artificial Intelligence to Gastroenterology and Hepatology.人工智能在胃肠病学和肝脏病学中的应用。
Gastroenterology. 2020 Jan;158(1):76-94.e2. doi: 10.1053/j.gastro.2019.08.058. Epub 2019 Oct 5.
2
Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.机器学习模型对上消化道出血的预测能力优于临床风险评分系统的验证。
Gastroenterology. 2020 Jan;158(1):160-167. doi: 10.1053/j.gastro.2019.09.009. Epub 2019 Sep 25.
3
Improving Acute GI Bleeding Management Through Artificial Intelligence: Unnatural Selection?通过人工智能改善急性胃肠道出血管理：非自然选择？
Dig Dis Sci. 2019 Aug;64(8):2061-2064. doi: 10.1007/s10620-019-05698-0.
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Glasgow Blatchford Score of limited benefit for low-risk urban patients: a mixed methods study.格拉斯哥布莱奇福德评分对低风险城市患者益处有限：一项混合方法研究。
Endosc Int Open. 2017 Oct;5(10):E950-E958. doi: 10.1055/s-0043-117880. Epub 2017 Sep 29.
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Validation of computational methods in genomics.基因组学中计算方法的验证。
Curr Genomics. 2007 Mar;8(1):1-19. doi: 10.2174/138920207780076956.
6
Risk scoring systems to predict need for clinical intervention for patients with nonvariceal upper gastrointestinal tract bleeding.预测非静脉曲张性上消化道出血患者临床干预需求的风险评分系统。
Am J Emerg Med. 2007 Sep;25(7):774-9. doi: 10.1016/j.ajem.2006.12.024.
7
A risk score to predict need for treatment for upper-gastrointestinal haemorrhage.预测上消化道出血治疗需求的风险评分
Lancet. 2000 Oct 14;356(9238):1318-21. doi: 10.1016/S0140-6736(00)02816-6.
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