Surgery – Diagnostic testing
Patent
1998-06-02
2000-08-08
Hindenburg, Max
Surgery
Diagnostic testing
128920, 600481, A61N 500
Patent
active
06099469&
ABSTRACT:
A reflex algorithm for assessing cardiac patients, which does not require human decision-making in selecting assays to be performed. Performance of further biochemical marker tests on a patient is dependent upon the outcome of previously conducted tests. The reflex algorithm focuses on obtaining the most accurate test information as early as possible from several different tests run in very short intervals. The reflex algorithm may be implemented by a computer that can copy requests for the most appropriate test to the loadlists of automated clinical chemistry and immunoassay analyzers. Also provided is an integrated platform for executing clinical chemistry assays and immunoassays according to a reflex algorithm.
REFERENCES:
patent: 5690103 (1997-11-01), Groth et al.
patent: B15747274 (1999-08-01), Jackowski
Creation and Implementation of a Clinical Algorithm for Acute Myocardial Infarction, Caragher, T.E., Fernandez B.B., Arnold M., Barr, L.A.; Clinical Chemistry, vol. 43, No. 6, p. S108, (1997).
Early Assessment Of Patients With Suspected Acute Myocardial Infarction By Biochemical Monitoring And Neural Network Analysis, Johan Ellenius, Torgny Groth, Bertil Lindahl and Lars Wallentin, Clinical Chemistry 43:10, pp. 1919-1925 (1997).
Assessment Of Sensitive Thyrotropin Assays For An Expanded Role In Thyroid Function Testing: Proposed Criteria For Analytic Performance And Clinical Utility; George G. Klee and Jan D. Hay, Journal of Clinical Endocrinology and Metabolism, vol. 64, No. 3, pp. 641-671 (1987).
Use Of Neural Networks To Diagnose Acute Myocardial Infarction. II. A Clinical Application, Susan M. Pedersen, Jorgen S. Jorgensen, J. Boiden Pederson, Clinical Chemistry vol. 42:4, pp. 613-617 (1996).
Early Diagnosis And Exclusion Of Acute Myocardial Infarction Using Biochemical Monitoring, Bertil Lindahl, Per Venge, and Lars Wallentin, Coronary and Heart Disease, vol. 6, pp. 321-328 (1995).
Use Of An Artificial Neural Network For The Diagnosis Of Myocardial Infarction, William G. Baxt, Annals of Internal Medicine, vol. 115, pp. 843-848 (1991).
Early Diagnosis Of Myocardial Infraction By Timed Sequential Enzyme Measurements, P. O. Collinson, S. B. Rosalki M. Flather, R. Wolman and T. Evans, Annals of Clinical Biochemistry, vol. 25, pp. 376-382 (1988).
Analysis Of The Clinical Variables Driving Decision In An Artificial Neural Network Trained To Identify The Presence of Myocardial infraction, William G. Baxt, Journal of the American College of Emergency Physicians and the Society for Academic Emergency Medicine, vol. 21, pp. 1439-1444 (1992).
Neural Network Analysis Of Serial Cardiac Enzyme Data, A Clinical Application Of Artificial Machine Intelligence, James W. Furlong, Milton E. Dupuy and James A. Heinsimer, American Journal of Clinical Pathology, vol. 96, pp. 134-141 (1991).
Prospective Evaluation Of An EDB-Based Diagnostic Program To Be Used In Patients Admitted To Hospital With Acute Chest Pain, J. Jonsbu, O. Aase, A. Rollag, K. Liestol and J. Erikssen, European Heart Journal, vol. 14, pp. 441-446 (1993).
Ruling Out Acute Myocardial Infarction, A Prospective Multicenter Validation Of A 12-Hour Strategy For Patients At Low Risk, Thomas H. Lee, Gregory Juarex, E. Francis Cook, Monica C. Weisberg, Gregoary W. Rouan, Donald A. Brand and Lee Goldman, New England Journal Of Medicine, vol. 324, No. 18, pp. 1239-1246 (1991)
Armstrong E. Glenn
Petry Christoph
Wagner Gerald
Wu Alan
Astorino Michael
Hindenburg Max
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