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Information Storage and Retrieval

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Time-sensitive clinical concept embeddings learned from large electronic health records.

BMC medical informatics and decision making
BACKGROUND: Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependenc...

An ontological framework for the formalization, organization and usage of TCM-Knowledge.

BMC medical informatics and decision making
BACKGROUND: The traditional Chinese Medicine Language System (TCMLS) is a large-scale terminology system, developed from 2002 on by the Institute of Information of Traditional Chinese Medicine (IITCM). Until now, more than 120,000 concepts, 300,000 t...

Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification.

BMC medical informatics and decision making
BACKGROUND: Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identification, is important for man...

Developing a portable natural language processing based phenotyping system.

BMC medical informatics and decision making
BACKGROUND: This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches.

Extracting health-related causality from twitter messages using natural language processing.

BMC medical informatics and decision making
BACKGROUND: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In...

Natural language processing of radiology reports for identification of skeletal site-specific fractures.

BMC medical informatics and decision making
BACKGROUND: Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient populations w...

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system.

BMJ open
OBJECTIVE: Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techn...

Visual Exploration of Neural Document Embedding in Information Retrieval: Semantics and Feature Selection.

IEEE transactions on visualization and computer graphics
Neural embeddings are widely used in language modeling and feature generation with superior computational power. Particularly, neural document embedding - converting texts of variable-length to semantic vector representations - has shown to benefit w...

Classifying Biomedical Figures by Modality via Multi-Label Learning.

IEEE journal of biomedical and health informatics
The figures found in biomedical literature are a vital part of biomedical research, education, and clinical decision. The multitude of their modalities and the lack of corresponding metadata constitute search and information, retrieval a difficult ta...

MCN: A comprehensive corpus for medical concept normalization.

Journal of biomedical informatics
Normalization of clinical text involves linking different ways of talking about the same clinical concept to the same term in the standardized vocabulary. To date, very few annotated corpora for normalization have been available, and existing corpora...