Literature DB >> 16548951

Orthogonal search-based rule extraction for modelling the decision to transfuse.

T A Etchells1, M J Harrison.   

Abstract

Data from an audit relating to transfusion decisions during intermediate or major surgery were analysed to determine the strengths of certain factors in the decision making process. The analysis, using orthogonal search-based rule extraction (OSRE) from a trained neural network, demonstrated that the risk of tissue hypoxia (ROTH) assessed using a 100-mm visual analogue scale, the haemoglobin value (Hb) and the presence or absence of on-going haemorrhage (OGH) were able to reproduce the transfusion decisions with a joint specificity of 0.96 and sensitivity of 0.93 and a positive predictive value of 0.9. The rules indicating transfusion were: 1. ROTH > 32 mm and Hb < 94 g x l(-1); 2. ROTH > 13 mm and Hb < 87 g x l(-1); 3. ROTH > 38 mm, Hb < 102 g x l(-1) and OGH; 4. Hb < 78 g x l(-1).

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 16548951     DOI: 10.1111/j.1365-2044.2006.04545.x

Source DB:  PubMed          Journal:  Anaesthesia        ISSN: 0003-2409            Impact factor:   6.955


  2 in total

1.  Artificial Intelligence and Machine Learning in Anesthesiology.

Authors:  Christopher W Connor
Journal:  Anesthesiology       Date:  2019-12       Impact factor: 7.892

2.  Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome after hospital discharge using neural networks.

Authors:  Valérie Bourdès; Jean Ferrières; Jacques Amar; Elisabeth Amelineau; Stéphane Bonnevay; Maryse Berlion; Nicolas Danchin
Journal:  Med Biol Eng Comput       Date:  2011-05-20       Impact factor: 2.602

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.