Enhancing text classification to improve information filtering

Text classification, information filtering, semi-supervised learning, quality control

Partially supervised learning of fuzzy classification rules

Fuzzy classification, semi-supervised learning, data mining

Validation of the Killip-Kimball Classification and Late Mortality after Acute Myocardial Infarction

Background: The classification or index of heart failure severity in patients with acute myocardial infarction (AMI) was proposed by Killip and Kimball aiming at assessing the risk of in-hospital death and the potential benefit of specific management of care provided in Coronary Care Units (CCU) during the decade of 60. Objective: To validate the risk stratification of Killip classification in the long-term mortality and compare the prognostic value in patients with non-ST-segment elevation MI (NSTEMI) relative to patients with ST-segment elevation MI (STEMI), in the era of reperfusion and modern antithrombotic therapies. Methods: We evaluated 1906 patients with documented AMI and admitted to the CCU, from 1995 to 2011, with a mean follow-up of 05 years to assess total mortality. Kaplan-Meier (KM) curves were developed for comparison between survival distributions according to Killip class and NSTEMI versus STEMI. Cox proportional regression models were developed to determine the independent association between Killip class and mortality, with sensitivity analyses based on type of AMI. Results: The proportions of deaths and the KM survival distributions were significantly different across Killip class >1 (p <0.001) and with a similar pattern between patients with NSTEMI and STEMI. Cox models identified the Killip classification as a significant, sustained, consistent predictor and independent of relevant covariables (Wald χ2 16.5 [p = 0.001], NSTEMI) and (Wald χ2 11.9 [p = 0.008], STEMI). Conclusion: The Killip and Kimball classification performs relevant prognostic role in mortality at mean follow-up of 05 years post-AMI, with a similar pattern between NSTEMI and STEMI patients.

Prototype-based classification and clustering

Magdeburg, Univ., Fak. für Informatik, Habil.-Schr., 2006

Visual analysis, clustering, and classification of contrast-enhanced tumor perfusion MRI data

Magdeburg, Univ., Fak. für Inf., Diss., 2014

A tentative classification of the palearctic unionids.

v.24:no.11(1940)

A geographic classification of Neotropical mammals.

v.36:no.6(1958)

On the classification of the histerid beetles, by Rupert L. Wenzel.

v.28:no.2(1944)

Evolution and classification of the Osteostraci. The exoskeleton of early Osteostraci. / Robert H. Denison --

v.11:no.3-4(1951)

Revised classification of Pteraspididae with description of new forms from Wyoming / [by] Robert H. Denison --

v.20:no.1(1970)

Investigation of the classification of the rodent genus Eumys from the Middle Oligocene of the Big Badlands of South Dakota using multivariate statistical analysis / Sue Vilhauer Rosser --.

v.39:no.3(1978)

Current classification and family-group names in Staphyliniformia (Coleoptera) / Alfred F. Newton, Jr., Margaret K. Thayer.

n.s. no.67(1992)

Fish assemblages in two sandy beaches in lower Purus river, Amazonas, Brazil

Fish assemblages from two sandy beaches in the lower Purus river (Amazonas, Brazil) were compared. Four sampling groups were represented by: day and night samples in sandy beach inside the Reserva Biológica de Abufari (biological reserve) and day and night samples in the Reserva de Desenvolvimento Sustentável Piagaçu-Purus (sustainable development reserve). Samples were collected during low water levels (November) in 2007. The fish were sampled by means of seines with mesh size of 5 mm between opposing knots, 11 m long and 6 m wide. A total of 112 fish species belonging to nine orders and 27 families was captured. The vast majority of the dominant forms consisted of small fishes (< 100 mm SL) or juveniles. Samples collected in Abufari at night presented more specimens (3,540), higher richness (84 spp.), larger total biomass (76,614 g) and higher diversity (H'= 2.57) than the other groups. The composition of fish assemblages was significantly different among all analyzed groups (ANOSIM, p < 0.0001, R= 0.71). NMDS analysis also clustered all species in four distinct groups according to species composition per period and site. SIMPER analyses showed that 80% of variation of species composition among the groups examined was due to 12 species. However, fish composition did not show any correlation with the abiotic factors examined. Different levels of use in both reserves may explain differences in fish composition.