Biomedical subjects

Afsaneh Barzi

Publications and source records attributed to Afsaneh Barzi.

3 recordsLinked to original sources

PubMed2005

A Bayesian model for triage decision support.

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.

Abdominal Pain↗

PubMed2005

Ontology Driven Construction of a Knowledgebase for Bayesian Decision Models Based on UMLS.

All decision models use some form of language to describe domain elements and their interactions. The terminology is often specific and even unique to the algorithm and is a choice of designers. Nevertheless the domain elements and concepts of any decision problem are almost never unique and are used and reused in many other decision problems. The same is true about the information about those elements in the context of different decision problems. Put together, the information about any given element forms our knowledge about the element and if stored properly in a knowledgebase, can be used and reused as necessary without the need for duplication.In this paper we discuss creation of an ontology using UMLS vocabulary and semantic network that provides an abstract understanding of elements (or objects) in the problem domain. Based on this ontology, a knowledgebase will be constructed that provides further information about the object in relation to another object or objects as described in the semantic links.A knowledgebase structured as such will have the benefit of problem-independence. It can be expanded as needed to include other objects that are used in a different series of problems and therefore, will have a one to many mapping between knowledgebase and decision models. Updating the knowledgebase will update the decision models seamlessly and maintenance will be less of an issue across decision models and within the knowledgebase. We are using this approach in building Bayesian decision models using Bayesian networks; however, this approach is not limited to Bayesian networks and has been and can be used for other decision making purposes.

Bayes Theorem↗

PubMed2002

Self-administered decision support tool for triage: results of a retrospective study.

BACKGROUND: This study was designed to evaluate the safety of a self-administered triage tool. MATERIALS: Ninety-five patients older than 14 years who presented to Memorial Hermann Hospital emergency room (ER) with chief complaint of abdominal pain were included in the study. Their ER disposition and final diagnoses were logged into a database. The assigned disposition and top three diagnoses by the triage tool for each patient were also logged into the database. An emergency physician blinded to the actual disposition reviewed all cases and provided a disposition for each patient. RESULTS: The system disposed 51.1% of cases appropriately and under-disposed 4.4% of cases. Comparison between the system and the emergency physician shows that all cases under-disposed by the system are also under-disposed by the physician.

Abdominal Pain↗