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Proc AMIA Symp. 2000:225-9.
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本文引用的文献

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Comparative Analysis of Coronary Surgery Risk Stratification Models.冠状动脉手术风险分层模型的比较分析
J Invasive Cardiol. 1997 Apr;9(3):203-222.
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The 1996 coronary artery bypass risk model: the Society of Thoracic Surgeons Adult Cardiac National Database.1996年冠状动脉搭桥手术风险模型：胸外科医师协会成人心脏国家数据库
Ann Thorac Surg. 1999 Apr;67(4):1205-8. doi: 10.1016/s0003-4975(99)00206-4.
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New advances and validation of knowledge management tools for critical care using classifier techniques.使用分类技术的重症监护知识管理工具的新进展与验证
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1995 coronary artery bypass risk model: The Society of Thoracic Surgeons Adult Cardiac National Database.
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Coronary artery bypass risk prediction using neural networks.使用神经网络进行冠状动脉搭桥风险预测。
Ann Thorac Surg. 1997 Jun;63(6):1635-43. doi: 10.1016/s0003-4975(97)00225-7.
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Use of a probabilistic neural network to estimate the risk of mortality after cardiac surgery.使用概率神经网络估计心脏手术后的死亡风险。
Med Decis Making. 1997 Apr-Jun;17(2):178-85. doi: 10.1177/0272989X9701700208.
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Calculating risk and outcome: The Society of Thoracic Surgeons database.计算风险与结果：胸外科医师协会数据库
Ann Thorac Surg. 1996 Nov;62(5 Suppl):S2-5; discussion S31-2. doi: 10.1016/0003-4975(96)00818-1.
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Coronary artery bypass grafting: the Society of Thoracic Surgeons National Database experience.冠状动脉搭桥术：胸外科医师协会国家数据库经验
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Neural net-bootstrap hybrid methods for prediction of complications in patients implanted with artificial heart valves.用于预测人工心脏瓣膜植入患者并发症的神经网络-自助法混合方法。
J Heart Valve Dis. 1994 Jan;3(1):49-52.
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The theorem of Bayes as a clinical research tool.作为临床研究工具的贝叶斯定理。
Surg Gynecol Obstet. 1987 Aug;165(2):127-9.

利用神经网络进行选择性采样以克服先验概率的偏差。

Selective sampling to overcome skewed a priori probabilities with neural networks.

作者信息

Ennett C M, Frize M

机构信息

Carleton University, Ottawa, ON, Canada.

出版信息

Proc AMIA Symp. 2000:225-9.

PMID:11079878
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2243711/
Abstract

Highly skewed a priori probabilities present challenges for researchers developing medical decision aids due to a lack of information on the rare outcome of interest. This paper attempts to overcome this obstacle by artificially increasing the mortality rate of the training sets. A weight pruning technique called weight-elimination is also applied to this coronary artery bypass grafting (CABG) database to assess its impact on the artificial neural network's (ANN) performance. The results showed that increasing the mortality rate improved the sensitivity rates at the cost of the other performance measures, and the weight-elimination cost function improved the sensitivity rate without seriously affecting the other performance measures.

摘要

由于缺乏关于感兴趣的罕见结果的信息，高度偏态的先验概率给开发医学决策辅助工具的研究人员带来了挑战。本文试图通过人为提高训练集的死亡率来克服这一障碍。一种称为权重消除的权重修剪技术也应用于这个冠状动脉旁路移植术（CABG）数据库，以评估其对人工神经网络（ANN）性能的影响。结果表明，提高死亡率以牺牲其他性能指标为代价提高了敏感度，而权重消除成本函数提高了敏感度，同时没有严重影响其他性能指标。