Literature DB >> 17890063

The TREAT project: decision support and prediction using causal probabilistic networks.

Leonard Leibovici1, Mical Paul, Anders D Nielsen, Evelina Tacconelli, Steen Andreassen.   

Abstract

TREAT is a decision support system for antibiotic treatment in inpatients with common bacterial infections. It was tested in a randomised controlled trial in three countries and shown to improve the percentage of appropriate empirical antibiotic treatments, while at the same time reducing hospital stay and the use of broad-spectrum antibiotics. TREAT is based on a causal probabilistic network and uses a cost-benefit model for antibiotic treatment, including costs assigned to future resistance. In the present review we discuss the advantages of using causal probabilistic models for prediction and decision support, and the various decisions that were taken in the TREAT project.

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Year:  2007        PMID: 17890063     DOI: 10.1016/j.ijantimicag.2007.06.035

Source DB:  PubMed          Journal:  Int J Antimicrob Agents        ISSN: 0924-8579            Impact factor:   5.283


  6 in total

Review 1.  Systematic review and meta-analysis of the efficacy of appropriate empiric antibiotic therapy for sepsis.

Authors:  Mical Paul; Vered Shani; Eli Muchtar; Galia Kariv; Eyal Robenshtok; Leonard Leibovici
Journal:  Antimicrob Agents Chemother       Date:  2010-08-23       Impact factor: 5.191

2.  Developing clinical decision support within a commercial electronic health record system to improve antimicrobial prescribing in the neonatal ICU.

Authors:  R S Hum; K Cato; B Sheehan; S Patel; J Duchon; P DeLaMora; Y H Ferng; P Graham; D K Vawdrey; J Perlman; E Larson; L Saiman
Journal:  Appl Clin Inform       Date:  2014-04-09       Impact factor: 2.342

3.  Enhancing the fever workup utilizing a multi-technique modeling approach to diagnose infections more accurately.

Authors:  Adam M A Fadlalla; Joseph F Golob; Jeffrey A Claridge
Journal:  Surg Infect (Larchmt)       Date:  2010-07-28       Impact factor: 2.150

4.  Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC).

Authors:  Marius Huehn; Jan Gaebel; Alexander Oeser; Andreas Dietz; Thomas Neumuth; Gunnar Wichmann; Matthaeus Stoehr
Journal:  Cancers (Basel)       Date:  2021-11-23       Impact factor: 6.639

Review 5.  The antibiogram: key considerations for its development and utilization.

Authors:  William R Truong; Levita Hidayat; Michael A Bolaris; Lee Nguyen; Jason Yamaki
Journal:  JAC Antimicrob Resist       Date:  2021-05-25

6.  Supervised learning for infection risk inference using pathology data.

Authors:  Bernard Hernandez; Pau Herrero; Timothy Miles Rawson; Luke S P Moore; Benjamin Evans; Christofer Toumazou; Alison H Holmes; Pantelis Georgiou
Journal:  BMC Med Inform Decis Mak       Date:  2017-12-08       Impact factor: 2.796

  6 in total

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