A survey of the slender loris Loris tardigradus, a Vulnerable primate, was carried out in the Dindigul Forests of Tamil Nadu, India, in 1996 in order to assess prevalence. Lorises were found in high densities in the open Euphorbia scrub forests and in crop lands nearby. Although most often seen in bushes and Acacia trees, the species uses a wide variety of vegetation and substrates. Individuals were found mostly at an altitude of 300–500 m. Body measurements taken on sample animals revealed that the population belongs to the subspecies L. t. lydekkerianus. Conservation measures for the species in this region include additional surveys and evaluation of prospects for establishing a slender loris sanctuary.
Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.
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