Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. - Test2 - elhamkhani - TriplyDB

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

http://www.wikidata.org/entity/Q34371773

about

SHRINE: enabling nationally scalable multi-site disease studies KneeTex: an ontology-driven system for information extraction from MRI reports Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project Extracting information from the text of electronic medical records to improve case detection: a systematic review Natural language processing: an introduction An autism case history to review the systematic analysis of large-scale data to refine the diagnosis and treatment of neuropsychiatric disorders Biomedical relation extraction: from binary to complex Coreference resolution: a review of general methodologies and applications in the clinical domain Mining electronic health records: towards better research applications and clinical care Text mining resources for the life sciences.Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1 Clinical research informatics: a conceptual perspective Network analysis of unstructured EHR data for clinical research Developing a natural language processing application for measuring the quality of colonoscopy procedures Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations Trends in biomedical informatics: automated topic analysis of JAMIA articles Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2 Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources Combining knowledge- and data-driven methods for de-identification of clinical narratives A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.Automatically Detecting Acute Myocardial Infarction Events from EHR Text: A Preliminary Study Detection of sentence boundaries and abbreviations in clinical narratives Dynamic clinical data mining: search engine-based decision support Big data in medicine is driving big changes Standardizing adverse drug event reporting data Chronology of your health events: approaches to extracting temporal relations from medical narratives Processing biological literature with customizable Web services supporting interoperable formats Discovering body site and severity modifiers in clinical texts A corpus-based approach for automated LOINC mapping Cross-sectional relatedness between sentences in breast radiology reports: development of an SVM classifier and evaluation against annotations of five breast radiologists Evaluating temporal relations in clinical text: 2012 i2b2 Challenge BoB, a best-of-breed automated text de-identification system for VHA clinical documents Generalizability and comparison of automatic clinical text de-identification methods and resources Mining the pharmacogenomics literature--a survey of the state of the art The MiPACQ clinical question answering system A cloud-based approach to medical NLP Trends in biomedical informatics: most cited topics from recent years

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Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

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2010 nî lūn-bûn

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2010 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած

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2010 թվականի սեպտեմբերին հրատարակված գիտական հոդված

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Christopher G Chute

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Guergana K Savova

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Jiaping Zheng

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Karin C Kipper-Schuler

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Philip V Ogren

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Sunghwan Sohn

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P2860

caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research

Mayo clinic NLP system for patient smoking status identification

The NLM Indexing Initiative.

Exploring semantic groups through visual approaches.

Empirical data on corpus design and usage in biomedical natural language processing.

ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.

Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.

Word sense disambiguation across two domains: biomedical literature and clinical notes.

Extracting information from textual documents in the electronic health record: a review of recent research.

Recognizing obesity and comorbidities in sparse data.

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