AIMC Topic: Semantics

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Quantitative analysis of manual annotation of clinical text samples.

International journal of medical informatics
BACKGROUND: Semantic interoperability of eHealth services within and across countries has been the main topic in several research projects. It is a key consideration for the European Commission to overcome the complexity of making different health in...

Biomedical semantic indexing by deep neural network with multi-task learning.

BMC bioinformatics
BACKGROUND: Biomedical semantic indexing is important for information retrieval and many other research fields in bioinformatics. It annotates biomedical citations with Medical Subject Headings. In face of unbalanced category distribution in the trai...

SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields.

BMC medical informatics and decision making
BACKGROUND: Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease...

Using Lexical Chains to Identify Text Difficulty: A Corpus Statistics and Classification Study.

IEEE journal of biomedical and health informatics
Our goal is data-driven discovery of features for text simplification. In this paper, we investigate three types of lexical chains: exact, synonymous, and semantic. A lexical chain links semantically related words in a document. We examine their pote...

Clinical text annotation - what factors are associated with the cost of time?

AMIA ... Annual Symposium proceedings. AMIA Symposium
Building high-quality annotated clinical corpora is necessary for developing statistical Natural Language Processing (NLP) models to unlock information embedded in clinical text, but it is also time consuming and expensive. Consequently, it important...

A Frame-Based NLP System for Cancer-Related Information Extraction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep le...

The Sublanguage of Clinical Problem Lists: A Corpus Analysis.

AMIA ... Annual Symposium proceedings. AMIA Symposium
.Summary-level clinical text is an important part of the overall clinical record as it provides a condensed and efficient view into the issues pertinent to the patient, or their "problem list." These problem lists contain a wealth of information pert...

Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document. Recent algorithms such as word2vec are capable of learning semantic meaning and similarity between words in an entirely uns...

Identifying Similar Non-Lattice Subgraphs in Gene Ontology based on Structural Isomorphism and Semantic Similarity of Concept Labels.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Non-Lattice Subgraphs (NLSs) are graph fragments of a terminology which violates the lattice property, a desirable property for a well-formed terminology. They have been proven to be useful in identifying inconsistencies in biomed-ical terminologies....

A Preliminary Study of Clinical Concept Detection Using Syntactic Relations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Concept detection is an integral step in natural language processing (NLP) applications in the clinical domain. Clinical concepts are detailed (e.g., "pain in left/right upper/lower arm/leg") and expressed in diverse phrase types (e.g., noun, verb, a...