AIMC Topic: Natural Language Processing

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Composition-driven symptom phrase recognition for Chinese medical consultation corpora.

BMC medical informatics and decision making
BACKGROUND: Symptom phrase recognition is essential to improve the use of unstructured medical consultation corpora for the development of automated question answering systems. A majority of previous works typically require enough manually annotated ...

MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks.

Computational and mathematical methods in medicine
Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of researches have com...

Leveraging medical context to recommend semantically similar terms for chart reviews.

BMC medical informatics and decision making
BACKGROUND: Information retrieval (IR) help clinicians answer questions posed to large collections of electronic medical records (EMRs), such as how best to identify a patient's cancer stage. One of the more promising approaches to IR for EMRs is to ...

MedTAG: a portable and customizable annotation tool for biomedical documents.

BMC medical informatics and decision making
BACKGROUND: Semantic annotators and Natural Language Processing (NLP) methods for Named Entity Recognition and Linking (NER+L) require plenty of training and test data, especially in the biomedical domain. Despite the abundance of unstructured biomed...

Vehicle Destination Prediction Using Bidirectional LSTM with Attention Mechanism.

Sensors (Basel, Switzerland)
Satellite navigation has become ubiquitous to plan and track travelling. Having access to a vehicle's position enables the prediction of its destination. This opens the possibility to various benefits, such as early warnings of potential hazards, rou...

Using natural language processing to understand people and culture.

The American psychologist
Language can provide important insights into people, and culture more generally. Further, the digitization of information has made more and more textual data available. But by itself, all that data are just that: data. Realizing its potential require...

Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset.

Nature communications
To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for p...

Prediction of solar cell materials via unsupervised literature learning.

Journal of physics. Condensed matter : an Institute of Physics journal
Despite the significant advancement of the data-driven studies for physical science, the textual data that are numerous in the literature are not fully embraced by the physics and materials community. In this manuscript, we successfully employ the na...

Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation.

Psychometrika
Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not id...

Extracting experimental parameter entities from scientific articles.

Journal of biomedical informatics
Systematic reviews are labor-intensive processes to combine all knowledge about a given topic into a coherent summary. Despite the high labor investment, they are necessary to create an exhaustive overview of current evidence relevant to a research q...