A facile synthesis of Zn-doped TiO nanoparticles with highly exposed (001) facets for enhanced photocatalytic performance.

It is a great challenge to simultaneously improve the visible light absorption capacity and enhance photon-generated carrier separation efficiency of photocatalysts. Herein, Zn-doped TiO nanoparticles with high exposure of the (001) crystal face were prepared a one-step hydrothermal decomposition method. A detailed analysis reveals that the electronic structures were modulated by Zn doping; thus, the responsive wavelength was extended to 600 nm, which effectively improved the visible light absorption of TiO.

Temporal indexing of medical entity in Chinese clinical notes.

Background: The goal of temporal indexing is to select an occurred time or time interval for each medical entity in clinical notes, so that all medical entities can be indexed on a united timeline, which could assist the understanding of clinical notes and the further application of medical entities. Some temporal relation shared tasks for the medical entity in English clinical notes have been organized in the past few years, such as the 2012 i2b2 NLP challenge, 2015 and 2016 clinical TempEval challenges. In these tasks, many heuristics rule-based and machine learning-based systems have been developed.

Entity recognition from clinical texts via recurrent neural network.

Background: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years.

De-identification of clinical notes via recurrent neural network and conditional random field.

De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published. The 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-scale and RDOC Individualized Domains (N-GRID) clinical natural language processing (NLP) challenge contains a de-identification track in de-identifying electronic medical records (EMRs) (i.e.

CMedTEX: A Rule-based Temporal Expression Extraction and Normalization System for Chinese Clinical Notes.

Time is an important aspect of information and is very useful for information utilization. The goal of this study was to analyze the challenges of temporal expression (TE) extraction and normalization in Chinese clinical notes by assessing the performance of a rule-based system developed by us on a manually annotated corpus (including 1,778 clinical notes of 281 hospitalized patients). In order to develop system conveniently, we divided TEs into three categories: direct, indirect and uncertain TEs, and designed different rules for each category of them.

An automatic system to identify heart disease risk factors in clinical texts over time.

Despite recent progress in prediction and prevention, heart disease remains a leading cause of death. One preliminary step in heart disease prediction and prevention is risk factor identification. Many studies have been proposed to identify risk factors associated with heart disease; however, none have attempted to identify all risk factors.

Automatic de-identification of electronic medical records using token-level and character-level conditional random fields.

De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informatics for Integrating Biology and Bedside) clinical natural language processing (NLP) challenge sets up a track for de-identification (track 1). In this study, we propose a hybrid system based on both machine learning and rule approaches for the de-identification track.