Hierarchical dirichlét learning - Filling in the thin spots in a database

Steen Andreassen, Brian Kristensen, Alina Zalounina, Leonard Leibovici, Uwe Frank, Henrik C. Schønheyder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Estimation of probabilities by classical maximum likelihood estimators can give unreliable results when the number of cases is small. A Bayesian approach, where prior probabilities with Dirichlet distributions are used to temper the estimates, can reduce the variance enough to make the estimates useful. This is demonstrated by using this approach to estimate mortalities of severe infections from different sites, lungs, skin urinary tract, etc. The prior probabilities are provided in a hierarchical way, i.e. by deriving them from the same database, but without distinguishing between different sites of infection.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 9th Conference on Artificial Intelligence in Medicine in Europe, AIME 2003, Proceedings
Pages274-283
Number of pages10
DOIs
StatePublished - 2003
Externally publishedYes
Event9th Conference on Artificial Intelligence on in Medicine in Europe, AIME 2003 - Protaras, Cyprus
Duration: 18 Oct 200322 Oct 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2780 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Conference on Artificial Intelligence on in Medicine in Europe, AIME 2003
Country/TerritoryCyprus
CityProtaras
Period18/10/0322/10/03

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