Confidence Predictions for the Diagnosis of Acute Abdominal Pain

机译：诊断急性腹痛的信心预测

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Most current machine learning systems for medical decision support do not produce any indication of how re] iable each of their predictions is. However, an indication of this kind is highly desirable especially in the medical field. This paper deals with this problem by applying a recently developed technique for assigning confidence measures to predictions, called conformal prediction, to the problem of acute abdominal pain diagnosis. The data used consist of a large number of hospital records of patients who suffered acute abdominal pain. Each record is described by 33 symptoms and is assigned to one of nine diagnostic groups. The proposed method is based on Neural Networks and for each patient it can produce either the most likely diagnosis together with an associated confidence measure, or the set of all possible diagnoses needed to satisfy a given level of confidence.

机译：当前，大多数用于医学决策支持的机器学习系统都无法显示其每个预测的可靠性。但是，特别是在医学领域，非常需要这种指示。本文通过应用一种最新开发的技术来解决此问题，该技术用于对急性腹痛诊断问题的预测（称为共形预测）分配置信度。所使用的数据包括遭受急性腹痛的患者的大量医院记录。每个记录由33个症状描述，并分配给9个诊断组之一。所提出的方法基于神经网络，并且可以为每个患者提供最可能的诊断以及相关的置信度度量，或者可以满足给定置信度水平所需的所有可能诊断的集合。

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《Artificial intelligence applications and innovations III》|2009年|175-184|共10页
会议地点 Thessaloniki(GR);Thessaloniki(GR)
作者
Harris Papadopoulos; Alex Gammerman; Volodya Vovk;
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Computer Science and Engineering Department, Frederick University, 7 Y. Frederickou St., Palou-riotisa, Nicosia 1036, Cyprus;

Department of Computer Science, Royal Holloway, University of London, Egham Hill, Egham, Surrey TW20 0EX, England;

Department of Computer Science, Royal Holloway, University of London, Egham Hill, Egham, Surrey TW20 0EX, England;

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正文语种 eng
中图分类人工智能理论;
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Confidence Predictions for the Diagnosis of Acute Abdominal Pain

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