1
artículo
Publicado 2009
Enlace
Enlace
In the paper, random forests and logistic regressions’ support of financial analysis functions’ predictive tool to forecast corporate performance and rank accounting and corporate variables according to their impact on performance is demonstrated. Ten-fold cross-validation experiments are conducted on one sample each of Latin American depository receipts (ADRs) and Latin American banks. Random forests indicate that the most important variables that affect ADRs performance are size and the law-and-order tradition; the most important variables that affect banks are size, long-term assets to deposits, number of directors, and efficiency of the legal system. The interpretation of predictive models for a small sample improved when the capacity of random forests to rank and predict with the parameters of a logistic regression were combined.
2
artículo
Publicado 2020
Enlace
Enlace
Background: The COVID‐19 outbreak has resulted in collision between patients infected with SARS‐CoV‐2 and those with cancer on different fronts. Patients with cancer have been impacted by deferral, modification, and even cessation of therapy. Adaptive measures to minimize hospital exposure, following the precautionary principle, have been proposed for cancer care during COVID‐19 era. We present here a consensus on prioritizing recommendations across the continuum of sarcoma patient care. Material and Methods: A total of 125 recommendations were proposed in soft‐tissue, bone, and visceral sarcoma care. Recommendations were assigned as higher or lower priority if they cannot or can be postponed at least 2–3 months, respectively. The consensus level for each recommendation was classified as “strongly recommended” (SR) if more than 90% of experts agreed, “recommended” (R)...
