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Unified Medical Language System

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Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation.

BMC medical informatics and decision making
BACKGROUND: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine lea...

Medical concept normalization in social media posts with recurrent neural networks.

Journal of biomedical informatics
Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard...

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

Journal of biomedical informatics
BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches a...

Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We...

Hybrid Semantic Analysis for Mapping Adverse Drug Reaction Mentions in Tweets to Medical Terminology.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Social networks, such as Twitter, have become important sources for active monitoring of user-reported adverse drug reactions (ADRs). Automatic extraction of ADR information can be crucial for healthcare providers, drug manufacturers, and consumers. ...

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

Artificial intelligence in medicine
Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In thi...

OBO to UML: Support for the development of conceptual models in the biomedical domain.

Journal of biomedical informatics
A conceptual model abstractly defines a number of concepts and their relationships for the purposes of understanding and communication. Once a conceptual model is available, it can also be used as a starting point for the development of a software sy...

Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study.

International journal of medical informatics
OBJECTIVE: To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients through use of an enhanced task-specific electronic medical record (EMR) interface powered by natural language processing (NLP) techn...

Vector representations of multi-word terms for semantic relatedness.

Journal of biomedical informatics
This paper presents a comparison between several multi-word term aggregation methods of distributional context vectors applied to the task of semantic similarity and relatedness in the biomedical domain. We compare the multi-word term aggregation met...

Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.

BMC medical informatics and decision making
BACKGROUND: The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constru...