Confidence Predictions for the Diagnosis of Acute Abdominal Pain

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.

[1]  Alexander Gammerman,et al.  Pattern Recognition and Density Estimation under the General i.i.d. Assumption , 2001, COLT/EuroCOLT.

[2]  W. Gasarch,et al.  The Book Review Column 1 Coverage Untyped Systems Simple Types Recursive Types Higher-order Systems General Impression 3 Organization, and Contents of the Book , 2022 .

[3]  Alexander Gammerman,et al.  Computationally Efficient Transductive Machines , 2000, ALT.

[4]  Alexander Gammerman,et al.  Transductive Confidence Machines for Pattern Recognition , 2002, ECML.

[5]  Vladimir Vovk,et al.  Comparing the Bayes and Typicalness Frameworks , 2001, ECML.

[6]  M Juhola,et al.  Comparison of different neural network algorithms in the diagnosis of acute appendicitis. , 1996, International journal of bio-medical computing.

[7]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[8]  L. Iliadis,et al.  ANN for prognosis of abdominal pain in childhood : use of fuzzy modelling for convergence estimation , 2008 .

[9]  Harris Papadopoulos,et al.  Inductive Confidence Machines for Regression , 2002, ECML.

[10]  Harris Papadopoulos,et al.  Normalized nonconformity measures for regression Conformal Prediction , 2008 .

[11]  Vladimir Vovk,et al.  Ridge Regression Confidence Machine , 2001, International Conference on Machine Learning.

[12]  Vassilis Moustakis,et al.  Deep assessment of machine learning techniques using patient treatment in acute abdominal pain in children , 1996, Artif. Intell. Medicine.

[13]  Konrad Lang,et al.  Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain - Acute Abdominal Pain Study Group , 1996, Artif. Intell. Medicine.

[14]  Mesut Remzi,et al.  Artificial neural networks for decision-making in urologic oncology. , 2003, Reviews in urology.

[15]  Harris Papadopoulos,et al.  Conformal Prediction with Neural Networks , 2007 .

[16]  Peter Kokol,et al.  Comparison of Three Databases with a Decision Tree Approach in the Medical Field of Acute Appendicitis , 2001, MedInfo.

[17]  Harris Papadopoulos.  Qualified Predictions for Large Data Sets , 2009, Knowl. Eng. Rev..

[18]  G. Kokkinakis,et al.  Computer aided diagnosis of breast cancer in digitized mammograms. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[19]  L Edenbrandt,et al.  Intelligent computer reporting 'lack of experience': a confidence measure for decision support systems. , 1998, Clinical physiology.

[20]  George C. Anastassopoulos,et al.  A non-symbolic implementation of abdominal pain estimation in childhood , 2008, Inf. Sci..

[21]  H. Cai,et al.  An Affine Invariant Region Detector Using the 4th Differential Invariant , 2007 .

[22]  C. Pattichis,et al.  Artificial Neural Networks in Medical Imaging Systems , 2005 .

[23]  Alexander Gammerman,et al.  Transduction with Confidence and Credibility , 1999, IJCAI.

[24]  A. Gammerman,et al.  Bayesian diagnostic probabilities without assuming independence of symptoms. , 1991, Methods of information in medicine.

[25]  Paulo J. G. Lisboa,et al.  A review of evidence of health benefit from artificial neural networks in medical intervention , 2002, Neural Networks.