Machine learning predicts cancer-associated deep vein thrombosis using clinically available variables.

Purpose: To develop and validate machine learning (ML) models for cancer-associated deep vein thrombosis (DVT) and to compare the performance of these models with the Khorana score (KS).

Methods: We randomly extracted data of 2100 patients with cancer between Jan. 1, 2017, and Oct.

Predicting disease risks by matching quantiles estimation for censored data.

In time to event data analysis, it is often of interest to predict quantities such as t-year survival rate or the survival function over a continuum of time. A commonly used approach is to relate the survival time to the covariates by a semiparametric regression model and then use the fitted model for prediction, which usually results in direct estimation of the conditional hazard function or the conditional estimating equation. Its prediction accuracy, however, relies on the correct specification of the covariate-survival association which is often difficult in practice, especially when patient populations are heterogeneous or the underlying model is complex.

[Characteristics of mass distributions of aerosol particle and its inorganic water-soluble ions in summer over a suburb farmland in Beijing].

Agricultural activity is one of the important sources of aerosol particle. To understand the mass distribution and sources of aerosol particle and its inorganic water-soluble ions in the suburb farmland of Beijing, particle samples were collected with a MOUDI cascade impactor in the summer of 2004 in a suburb vegetable field. The mass distributions of the particle and its inorganic water-soluble ions in the diameter range of 0.