Computer probability estimates of angiographic coronary artery disease: Transportability and comparison with cardiologists' estimates
A computer algorithm for estimating probabilities of any significant coronary obstruction and triple vessel/left main obstructions was derived, validated, and compared with the assessments of cardiac clinician angiographers. The algorithm performed at least as well as the clinicians when the latter...
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Veröffentlicht in: | Computers and biomedical research 1992-10, Vol.25 (5), p.468-485 |
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creator | Detrano, Robert Bobbio, Marco Olson, Harold Shandling, Adrian Ellestad, Myrvin H. Alegria, Eduardo Martinez-Caro, Diego Righetti, Alberto Janosi, Andras Steinbrunn, Walter Pfisterer, Matthias Sanna, Gianpietro Ferarri, Giovanni Gupp, Kern H. Schmid, Johann-Jakob Deckers, Jaap Le, Ha Minh Brezden, Oleh Colombo, Antonio |
description | A computer algorithm for estimating probabilities of any significant coronary obstruction and triple vessel/left main obstructions was derived, validated, and compared with the assessments of cardiac clinician angiographers. The algorithm performed at least as well as the clinicians when the latter knew the identity of the patients whose angiograms they had decided to perform. The clinicians were more accurate when they did not know the identity of the subjects but worked from tabulated objective data. Referral and value induced bias may affect physician judgment in assessing disease probability. Application of computer aids or consultation with cardiologists not directly involved with patient management may assist in more rational assessments and decision making. |
doi_str_mv | 10.1016/0010-4809(92)90004-T |
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The algorithm performed at least as well as the clinicians when the latter knew the identity of the patients whose angiograms they had decided to perform. The clinicians were more accurate when they did not know the identity of the subjects but worked from tabulated objective data. Referral and value induced bias may affect physician judgment in assessing disease probability. 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The algorithm performed at least as well as the clinicians when the latter knew the identity of the patients whose angiograms they had decided to perform. The clinicians were more accurate when they did not know the identity of the subjects but worked from tabulated objective data. Referral and value induced bias may affect physician judgment in assessing disease probability. Application of computer aids or consultation with cardiologists not directly involved with patient management may assist in more rational assessments and decision making.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biological and medical sciences</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Coronary Angiography</subject><subject>Coronary Disease - diagnosis</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Medical management aid. 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subjects | Adult Aged Aged, 80 and over Algorithms Artificial Intelligence Biological and medical sciences Computerized, statistical medical data processing and models in biomedicine Coronary Angiography Coronary Disease - diagnosis Female Humans Male Medical management aid. Diagnosis aid Medical sciences Middle Aged Predictive Value of Tests |
title | Computer probability estimates of angiographic coronary artery disease: Transportability and comparison with cardiologists' estimates |
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