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Confidence Predictions for the Diagnosis of Acute Abdominal Pain

  • Harris Papadopoulos
  • Alex Gammerman
  • Volodya Vovk
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)

Abstract

Most current machine learning systems for medical decision support do not produce any indication of how reliable 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.

Keywords

Acute Abdominal Pain Perforated Peptic Ulcer Computational Learn Theory Conformal Predictor Conformal Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Harris Papadopoulos
    • 1
  • Alex Gammerman
    • 2
  • Volodya Vovk
    • 2
  1. 1.Computer Science and Engineering DepartmentFrederick UniversityPalou-riotisa, NicosiaCyprus
  2. 2.Department of Computer Science, Royal HollowayUniversity of London, Egham Hill, EghamSurreyEngland

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