B. H. Pollock. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovasc. Rev. Rep. 4, pages 367--375, 1983. Home/Search Document Not in Database Summary Related Articles Check |
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Reliable Diagnostics for Coronary Artery Disease - Matjaz Kukar University (Correct)
....the Nuclear Medicine Department between 1991 and 1994. We selected only the patients with complete diagnostic procedures (all four levels) 6] For determining the pre test probability we applied a tabular calculation, consisting of three attributes (age, sex, type of chest pain) retrieved from [9]. What is small enough depends on the problem and is calculated by internal leave one out testing 3. Results We performed all our experiments by using naive Bayesian classifier [4] as our ML tool of choice, since it usually performs very well in medical problems and can comprehensibly ....
B. H. Pollock. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovasc. Rev. Rep. 4, pages 367--375, 1983.
Machine Learning in Stepwise Diagnostic Process - Kukar, Groselj (1999) (Correct)
....2, which we reTable 1. IHD data for different diagnostic levels. Diagnostic data Number of attributes Nominal Numeric Total Signs, symptoms and history 23 7 30 Exercise ECG 7 9 16 Myocardial scintigraphy 22 9 31 Entropy of the dataset (229 pos. 98 neg. 0. 88 bit trieved from literature [10]. For each patient, the table was indexed by a subset of signs and symptoms attributes (age, sex, type of chest pain) The aim of our Table 2. Pretest probabilities for the presence of IHD Sex Age Asymptomatic Nonang. Atypical Typical patients chest pain angina angina Female 35 44 0.007 0.027 ....
B. H. Pollock. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovasc. Rev. Rep. 4, pages 367--375, 1983.
Transductive Reliability Estimation for Medical Diagnosis - Kukar (2002) (Correct)
....who were examined at 598 the Nuclear Medicine Department between 1991 and 1994. We selected only the 599 patients with complete diagnostic procedures (all four levels) 19] 600 [Table 3 about here. 601 For determining the pre test probability the physicians applied the Tab. 4 taken 602 from [25], which was calculated from the results of several studies on thousands 603 of patients. For each patient, the table was indexed by a subset of signs and 604 symptoms attributes (age, sex, type of chest pain) 605 [Table 4 about here. 606 29 The motivation for the original CAD study [10, 19] ....
B. H. Pollock. Computer-assisted interpretation of noninvasive tests for diagnosis 831 of coronary artery disease. Cardiovasc. Rev. Rep. 4, pages 367--375, 1983. 832
Making Reliable Diagnoses with Machine Learning: A Case Study - Kukar (2001) (Correct)
....of approximately 4000 patients who were examined at the Nuclear Medicine Department between 1991 and 1994. We selected only the patients with complete diagnostic procedures (all four levels) 8] In determining the pre test probability we applied the Table 2, which we retrieved from literature [11]. For each patient, the table was indexed by a subset of signs and symptoms attributes (age, sex, type of chest pain) The aim of our early studies [6, 7] was to improve the diagnostic performance (sensitivity and specificity) of non invasive diagnostic methods (i.e. clinical ex Table 1. CAD ....
B. H. Pollock. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovasc. Rev. Rep. 4, pages 367--375, 1983.
Transductive Reliability Estimation for Medical Diagnosis - Kukar (2002) (Correct)
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B. H. Pollock. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovasc. Rev. Rep. 4, pages 367--375, 1983.
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