Literature DB >> 16140572

A Bayesian model for triage decision support.

Sarmad Sadeghi1, Afsaneh Barzi, Navid Sadeghi, Brent King.   

Abstract

OBJECTIVE: To compare triage decisions of an automated emergency department triage system with decisions made by an emergency specialist.
METHODS: In a retrospective setting, data extracted from charts of 90 patients with chief complaint of non-traumatic abdominal pain were used as input for triage system and emergency medicine specialist. The final disposition and diagnoses of the physicians who visited the patient in Emergency Department (ED) as reflected in the medical records were considered as control. Results were compared by chi(2)-test and a binary logistic regression model.
RESULTS: Compared to emergency medicine specialist, triage system had higher sensitivity (90% versus 64%) and lower specificity (25% versus 48%) for patients who required hospitalization. The triage system successfully predicted the Admit decisions made in the ED whereas the emergency medicine specialist decisions could not predict the ED disposition. Both triage system and emergency medicine specialist properly disposed 56% of cases, however, the emergency medicine specialist in this study under-disposed more patients than the triage system considering Admit disposition (p=0.004) while he appropriately discharged more patients compared to the triage system (p=0.017).
CONCLUSION: The triage system studied here shows promise as a triage decision support tool to be used for telephone triage and triage in the emergency departments. This technology may also be useful to the patients as a self-triage tool. However, the efficiency of this particular application of this technology is unclear.

Entities:  

Mesh:

Year:  2005        PMID: 16140572     DOI: 10.1016/j.ijmedinf.2005.07.028

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  12 in total

1.  Classification of otoneurological cases according to Bayesian probabilistic models.

Authors:  Katja Miettinen; Martti Juhola
Journal:  J Med Syst       Date:  2010-04       Impact factor: 4.460

2.  [International outcomes from attempts to implement a clinical decision support system in gastroenterology].

Authors:  Josceli Maria Tenório; Anderson Diniz Hummel; Vera Lucia Sdepanian; Ivan Torres Pisa; Heimar de Fátima Marin
Journal:  J Health Inform       Date:  2011 Jan-Mar

Review 3.  Evolution and challenges in the design of computational systems for triage assistance.

Authors:  María M Abad-Grau; Jorge Ierache; Claudio Cervino; Paola Sebastiani
Journal:  J Biomed Inform       Date:  2008-02-05       Impact factor: 6.317

4.  A Bayesian Model to Predict Survival After Left Ventricular Assist Device Implantation.

Authors:  Manreet K Kanwar; Lisa C Lohmueller; Robert L Kormos; Jeffrey J Teuteberg; Joseph G Rogers; JoAnn Lindenfeld; Stephen H Bailey; Colleen K McIlvennan; Raymond Benza; Srinivas Murali; James Antaki
Journal:  JACC Heart Fail       Date:  2018-08-08       Impact factor: 12.035

5.  A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality.

Authors:  Natasha A Loghmanpour; Manreet K Kanwar; Marek J Druzdzel; Raymond L Benza; Srinivas Murali; James F Antaki
Journal:  ASAIO J       Date:  2015 May-Jun       Impact factor: 2.872

6.  A Semantic-Based Model for Triage Patients in Emergency Departments.

Authors:  Guilherme Wunsch; Cristiano A da Costa; Rodrigo R Righi
Journal:  J Med Syst       Date:  2017-03-10       Impact factor: 4.460

7.  A randomized controlled trial comparing health and quality of life of lung transplant recipients following nurse and computer-based triage utilizing home spirometry monitoring.

Authors:  Stanley M Finkelstein; Bruce R Lindgren; William Robiner; Ruth Lindquist; Marshall Hertz; Bradley P Carlin; Arin VanWormer
Journal:  Telemed J E Health       Date:  2013-10-01       Impact factor: 3.536

8.  Evaluation of symptom checkers for self diagnosis and triage: audit study.

Authors:  Hannah L Semigran; Jeffrey A Linder; Courtney Gidengil; Ateev Mehrotra
Journal:  BMJ       Date:  2015-07-08

9.  Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.

Authors:  Dhifaf Azeez; Mohd Alauddin Mohd Ali; Kok Beng Gan; Ismail Saiboon
Journal:  Springerplus       Date:  2013-08-29

10.  Cardiac Health Risk Stratification System (CHRiSS): a Bayesian-based decision support system for left ventricular assist device (LVAD) therapy.

Authors:  Natasha A Loghmanpour; Marek J Druzdzel; James F Antaki
Journal:  PLoS One       Date:  2014-11-14       Impact factor: 3.240

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