Neural networks as a prognostic tool of surgical risk in lung resections
Section snippets
Material and methods
Between 1992 and 1999, 141 consecutive patients (102 male, 39 female) underwent lung resection at the Thoracic Surgical Division of the Hospital de Clı́nicas from the Universidad de Buenos Aires. Mean age was 57.73 years (range, 16 to 84 years). Surgical procedures included 57 lobectomies, 40 pneumonectomies, and 44 wedge resections. All of them were standard, nonextended resections, with mediastinal node sampling.
Ninety-six preoperative clinical, laboratory, and spirometric categorical and
Results
Patient population was distributed in risk groups correlated with the Torrington and Goldman risk indices, as shown in Table 3. Postoperative mortality was progressively higher in patients in Torrington grades II and III, as shown in Table 4. There were four postoperative deaths among the 28 test patients.
The following dependent variables correlated with the independent variable postoperative death at a p less than 0.01 level: ventricular extrasystoles (p < 0.049; df = 1; F ratio, 3.93);
Comment
Medical applications of artificial NN are mostly based on their ability to handle classification problems: multiple examples are presented to the system together with the known answer, and the NN is thus allowed to learn by adaptation using various paradigms 8, 9, 10, 11, 12, 13, 14. Trained NN can then prospectively classify information from new patients. Neural networks learn by finding subtle association between multiple elements of information that are not immediately apparent to a trained
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