Confidence Predictions for the Diagnosis of Acute Abdominal Pain

Autor:	Volodya Vovk, Alexander Gammerman, Harris Papadopoulos
Rok vydání:	2009
Předmět:	medicine.medical_specialty Decision support system Artificial neural network Computer science Confidence measures medicine Acute abdominal pain Medical diagnosis Set (psychology) Intensive care medicine Field (computer science) Confidence interval
Zdroj:	IFIP Advances in Information and Communication Technology ISBN: 9781441902207 AIAI
DOI:	10.1007/978-1-4419-0221-4_22
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::79f0b99c3e2cbf5a7f7e3fd544f50b8a https://doi.org/10.1007/978-1-4419-0221-4_22 Zobrazit plný text záznamu