An automated method for determining which diseases are mentioned in biomedical text, the task of disease normalization. DNorm is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. DNorm is the first technique to use machine learning to normalize disease names and also the first method employing pairwise learning to rank in a normalization task.
A named entity recognition system intended primarily for biomedical text. BANNER uses conditional random fields as the primary recognition engine and includes a wide survey of the best techniques described in recent literature.
An ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information.
A hybrid method integrating a machine-learning model with a pattern identification strategy to identify the individual components of each composite mention. SimConcept achieves high performance in identifying and resolving composite mentions for three key biological entities: genes (90.42% in F-measure), diseases (86.47% in F-measure), and chemicals (86.05% in F-measure). SimConcept is the first text mining tool to systematically handle many types of composite mentions. It could be useful to assist the bioconcept normalization task.
A system for disease mention recognition and normalization in biomedical texts. AuDis utilizes an order two conditional random fields model. To optimize the results, we customize several post-processing steps, including abbreviation resolution, consistency improvement and stopwords filtering. As the official evaluation on the chemical disease relation (CDR) task in BioCreative V, it obtained the best performance (86.46% of F-score) among 40 runs on disease normalization of the disease named entity recognition (DNER) sub task. These results suggest that AuDis is a high-performance recognition system.
A web-based NCBI-PubMed search application, which can analyze articles for selected biomedical verbs and give users relational information, such as subject, object, location, manner, time, etc. After receiving keyword query input, BWS retrieves matching PubMed abstracts and lists them along with snippets by order of relevancy to protein-protein interaction. Users can then select articles for further analysis, and BWS will find and mark up biomedical relations in the text. The analysis results can be viewed in the abstract text or in table form.
Enables recognition and automatic encoding of clinical information in narrative patient reports. CLAMP is based on proven methods in many clinical Natural Language Processing (NLP) challenges. It is customizable thank to its capacities to offer components such as named entity recognition, assertion, UMLS encoder, and component customizations. The tool allows to annotate target documents, generate models, and process clinical notes.