12 research outputs found
Bulk Density Prediction for Histosols and Soil Horizons with High Organic Matter Content
The Level II aggregated forest soil condition database links soil physicochemical and hydraulic properties with long-term observations of forest condition in Europe
Is Percent âProjected Natural Vegetation Soil Carbonâ a Useful Indicator of Soil Condition?
Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings
Disaggregation of conventional soil map by generating multi realizations of soil class distribution (case study: Saadat Shahr plain, Iran)
High-resolution mapping of acid sulfate soils in Northern Australia through predictive models
Spatial analysis of clay content in soils using neurocomputing and pedological support: a case study of Valle Telesina (South Italy)
Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art
Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sourcesâsuch as biomedical literature, clinical narratives, product labeling, social media, and Web search logsâthat are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance