Recycled Jute Non-Woven Material Coated with Polyaniline/TiO Nanocomposite for Removal of Heavy Metal Ions from Water.

Growing volumes of textile waste and heavy metal pollution of water are emerging environmental challenges. In an attempt to tackle these issues, a non-woven sorbent based on jute fibers was fabricated by recycling the textile waste from the carpet industry. The influence of contact time, concentration, pH and temperature on the sorption of lead and copper ions from aqueous solutions was studied.

De-identification of clinical free text using natural language processing: A systematic review of current approaches.

Background: Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.

Evaluation of clinical named entity recognition methods for Serbian electronic health records.

Background And Objectives: The importance of clinical natural language processing (NLP) has increased with the adoption of electronic health records (EHRs). One of the critical tasks in clinical NLP is named entity recognition (NER). Clinical NER in the Serbian language is a severely under-researched area.

Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.

De-identification of clinical narratives is one of the main obstacles to making healthcare free text available for research. In this paper we describe our experience in expanding and tailoring two existing tools as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric evaluation notes for up to 25 different types of Protected Health Information (PHI). The methods we used rely on machine learning on either a large or small feature space, with additional strategies, including two-pass tagging and multi-class models, which both proved to be beneficial.

A competitive inhibitory circuit for selection of active and passive fear responses.

When faced with threat, the survival of an organism is contingent upon the selection of appropriate active or passive behavioural responses. Freezing is an evolutionarily conserved passive fear response that has been used extensively to study the neuronal mechanisms of fear and fear conditioning in rodents. However, rodents also exhibit active responses such as flight under natural conditions.

Combining knowledge- and data-driven methods for de-identification of clinical narratives.

A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Information (PHI). We describe the methods developed and evaluated as part of the i2b2/UTHealth 2014 challenge to identify PHI defined by 25 entity types in longitudinal clinical narratives. Our approach combines knowledge-driven (dictionaries and rules) and data-driven (machine learning) methods with a large range of features to address de-identification of specific named entities.

Using local lexicalized rules to identify heart disease risk factors in clinical notes.

Heart disease is the leading cause of death globally and a significant part of the human population lives with it. A number of risk factors have been recognized as contributing to the disease, including obesity, coronary artery disease (CAD), hypertension, hyperlipidemia, diabetes, smoking, and family history of premature CAD. This paper describes and evaluates a methodology to extract mentions of such risk factors from diabetic clinical notes, which was a task of the i2b2/UTHealth 2014 Challenge in Natural Language Processing for Clinical Data.

Ambiguity and variability of database and software names in bioinformatics.

Background: There are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning approaches to their identification.

Results: Through the development and analysis of a corpus of 60 full-text documents manually annotated at the mention level, we report high variability and ambiguity in database and software mentions.

Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.

Objective: Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier.