Knowledge base for evidence based medicine with bioinformatics components

Witold Jacak, Karin Pröll, Jerzy Rozenblit

Research output: Contribution to journalArticle

Abstract

This paper presents an approach for a multilevel knowledge base system for evidence-based medicine. A sequence of events called patient trial is extracted from computer patient records. These events describe one flow of therapy for a concrete disease. Each event is represented by state and time. We introduce a measure between states, which is used to calculate the best alignment between different patient trials. The alignment measure calculates the distance between two sequences of patient states, which represents the similarity of the course of disease. Based on that similarity- value classes are introduced by using specific clustering methods. These classes can be extended by gene expression data on micro-arrays leading to finer clustering containing similar trials - called trial families. For easy checking if a new trial belongs to a family we use profiles of Hidden Markov models to detect potential membership in a family.

Original languageEnglish (US)
Pages (from-to)163-173
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2809
StatePublished - Dec 1 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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