Use of an artificial neural network for the diagnosis of myocardial infarction.

Abstract:

OBJECTIVE:To validate prospectively the use of an artificial neural network to identify myocardial infarction in patients presenting to an emergency department with anterior chest pain. DESIGN:Prospective, blinded testing. SETTING:Tertiary university teaching center. PATIENTS:A total of 331 consecutive adult patients presenting with anterior chest pain. MEASUREMENTS:Diagnostic sensitivity and specificity with regard to the diagnosis of acute myocardial infarction. MAIN RESULTS:An artificial neural network was trained on clinical pattern sets retrospectively derived from the cases of 351 patients hospitalized with a high likelihood of having myocardial infarction. It was prospectively tested on 331 consecutive patients presenting to an emergency department with anterior chest pain. The ability of the network to distinguish patients with from those without acute myocardial infarction was compared with that of physicians caring for the same patients. The physicians had a diagnostic sensitivity of 77.7% (95% CI, 77.0% to 82.9%) and a diagnostic specificity of 84.7% (CI, 84.0% to 86.4%). The artificial neural network had a sensitivity of 97.2% (CI, 97.2% to 97.5%; P = 0.033) and a specificity of 96.2% (CI, 96.2% to 96.4%; P less than 0.001). CONCLUSION:An artificial neural network trained to identify myocardial infarction in adult patients presenting to an emergency department may be a valuable aid to the clinical diagnosis of myocardial infarction; however, this possibility must be confirmed through prospective testing on a larger patient sample.

journal_name

Ann Intern Med

authors

Baxt WG

doi

10.7326/0003-4819-115-11-843

subject

Has Abstract

pub_date

1991-12-01 00:00:00

pages

843-8

issue

11

eissn

0003-4819

issn

1539-3704

journal_volume

115

pub_type

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