AI Medical Compendium Topic:
Semantics

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Mapping between fMRI responses to movies and their natural language annotations.

NeuroImage
Several research groups have shown how to map fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI dat...

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

Computers in biology and medicine
Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interact...

DrugSemantics: A corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics.

Journal of biomedical informatics
For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care. The problem is tha...

Driving Under the Influence (of Language).

IEEE transactions on neural networks and learning systems
We present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports acquisition (learning grounded meanings of nouns and prepositions from human sentential annotation of robotic driving paths...

A deep learning approach for predicting the quality of online health expert question-answering services.

Journal of biomedical informatics
Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers ...

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

Journal of biomedical semantics
BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across dispara...

Dead simple OWL design patterns.

Journal of biomedical semantics
BACKGROUND: Bio-ontologies typically require multiple axes of classification to support the needs of their users. Development of such ontologies can only be made scalable and sustainable by the use of inference to automate classification via consiste...

RysannMD: A biomedical semantic annotator balancing speed and accuracy.

Journal of biomedical informatics
Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable the...

Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora.

Journal of biomedical informatics
OBJECTIVE: The goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised...

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

Computational intelligence and neuroscience
Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from...