A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU

Peter J.F. Lucas; Nicolette C. De Bruijn; Karin Schurink; Andy Hoepelman

doi:10.1016/S0933-3657(00)00048-8

A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU

Peter J.F. Lucas^*, Nicolette C. De Bruijn, Karin Schurink, Andy Hoepelman

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

84 Citations (Scopus)

Abstract

A probabilistic and decision-theoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the ICU is developed. Its probabilistic-network model includes temporal knowledge to diagnose pneumonia based on the likelihood of laryngotracheobronchial-tree colonization by pathogens, and symptoms and signs actually present in the patient. Optimal antimicrobial therapy is selected by balancing the expected efficacy of treatment.

Original language	English
Pages (from-to)	251-279
Number of pages	29
Journal	Artificial intelligence in medicine
Volume	19
Issue number	3
DOIs	https://doi.org/10.1016/S0933-3657(00)00048-8
Publication status	Published - Jul 2000
Externally published	Yes

Keywords

Bayesian networks
Decision theory
Infectious diseases
Intensive care
Medical decision support
Probabilistic networks
Temporal probabilistic models
n/a OA procedure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/S0933-3657(00)00048-8

Cite this

@article{bb39e6a02f4e4763942c5ee4e5b0338c,

title = "A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU",

abstract = "A probabilistic and decision-theoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the ICU is developed. Its probabilistic-network model includes temporal knowledge to diagnose pneumonia based on the likelihood of laryngotracheobronchial-tree colonization by pathogens, and symptoms and signs actually present in the patient. Optimal antimicrobial therapy is selected by balancing the expected efficacy of treatment.",

keywords = "Bayesian networks, Decision theory, Infectious diseases, Intensive care, Medical decision support, Probabilistic networks, Temporal probabilistic models, n/a OA procedure",

author = "Lucas, \{Peter J.F.\} and \{De Bruijn\}, \{Nicolette C.\} and Karin Schurink and Andy Hoepelman",

year = "2000",

month = jul,

doi = "10.1016/S0933-3657(00)00048-8",

language = "English",

volume = "19",

pages = "251--279",

journal = "Artificial intelligence in medicine",

issn = "0933-3657",

publisher = "Elsevier B.V.",

number = "3",

}

TY - JOUR

T1 - A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU

AU - Lucas, Peter J.F.

AU - De Bruijn, Nicolette C.

AU - Schurink, Karin

AU - Hoepelman, Andy

PY - 2000/7

Y1 - 2000/7

N2 - A probabilistic and decision-theoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the ICU is developed. Its probabilistic-network model includes temporal knowledge to diagnose pneumonia based on the likelihood of laryngotracheobronchial-tree colonization by pathogens, and symptoms and signs actually present in the patient. Optimal antimicrobial therapy is selected by balancing the expected efficacy of treatment.

AB - A probabilistic and decision-theoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the ICU is developed. Its probabilistic-network model includes temporal knowledge to diagnose pneumonia based on the likelihood of laryngotracheobronchial-tree colonization by pathogens, and symptoms and signs actually present in the patient. Optimal antimicrobial therapy is selected by balancing the expected efficacy of treatment.

KW - Bayesian networks

KW - Decision theory

KW - Infectious diseases

KW - Intensive care

KW - Medical decision support

KW - Probabilistic networks

KW - Temporal probabilistic models

KW - n/a OA procedure

UR - https://www.scopus.com/pages/publications/0034235303

U2 - 10.1016/S0933-3657(00)00048-8

DO - 10.1016/S0933-3657(00)00048-8

M3 - Article

C2 - 10906615

SN - 0933-3657

VL - 19

SP - 251

EP - 279

JO - Artificial intelligence in medicine

JF - Artificial intelligence in medicine

IS - 3

ER -

A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this