Using probabilistic and decision-theoretic methods in treatment and prognosis modeling

S. Andreassen, C. Riekehr, B. Kristensen, H. C. Schonheyder, L. Leibovici

Research output: Contribution to journalArticlepeer-review

Abstract

Causal probabilistic networks, also called Bayesian networks, allow both qualitative knowledge about the structure of a problem and quantitative knowledge, derived from case databases, expert opinion and literature to be exploited in the construction of decision support systems for diagnosis, treatment and prognosis. This mixing of qualitative and quantitative knowledge will be illustrated, using the selection of antibiotics for a subset of patients with severe infections. The subset consists of patients where bacteria or fungi have been found in the blood. A simple pathophysiological model of infection is used to calculate a prognosis, dependent on the choice of antibiotics. A decision-theoretic approach is used to balance the therapeutic benefit of antibiotic treatment against the cost of antibiotics in the form of direct monetary cost, side effects and ecological cost. A retrospective trial on patients with bacteria or fungi in the blood stemming from the urinary tract indicates that with this approach, it may be possible to suggest balanced choices of antibiotics that not only achieve greater therapeutic benefit, but also reduce the cost of therapy.

Original languageEnglish
Pages (from-to)121-134
Number of pages14
JournalArtificial Intelligence in Medicine
Volume15
Issue number2
DOIs
StatePublished - 1999
Externally publishedYes

Keywords

  • Antibiotic therapy
  • Bacteraemia
  • Causal probabilistic network
  • Decision support system
  • Decision theory
  • Prognosis

Fingerprint

Dive into the research topics of 'Using probabilistic and decision-theoretic methods in treatment and prognosis modeling'. Together they form a unique fingerprint.

Cite this