AI Medical Compendium Topic:
Semantics

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A disease inference method based on symptom extraction and bidirectional Long Short Term Memory networks.

Methods (San Diego, Calif.)
The wide applications of automatic disease inference in many medical fields improve the efficiency of medical treatments. Many efforts have been made to predict patients' future health conditions according to their full clinical texts, clinical measu...

Unsupervised word embeddings capture latent knowledge from materials science literature.

Nature
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the ma...

Neural Multimodal Cooperative Learning Toward Micro-Video Understanding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The prevailing characteristics of micro-videos result in the less descriptive power of each modality. The micro-video representations, several pioneer efforts proposed, are limited in implicitly exploring the consistency between different modality in...

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

Journal of biomedical informatics
BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations...

Adaptive Learning Emotion Identification Method of Short Texts for Online Medical Knowledge Sharing Community.

Computational intelligence and neuroscience
The medical knowledge sharing community provides users with an open platform for accessing medical resources and sharing medical knowledge, treatment experience, and emotions. Compared with the recipients of general commodities, the recipients in the...

Distant supervision for treatment relation extraction by leveraging MeSH subheadings.

Artificial intelligence in medicine
The growing body of knowledge in biomedicine is too vast for human consumption. Hence there is a need for automated systems able to navigate and distill the emerging wealth of information. One fundamental task to that end is relation extraction, wher...

Measuring the effect of different types of unsupervised word representations on Medical Named Entity Recognition.

International journal of medical informatics
BACKGROUND: This work deals with Natural Language Processing applied to the clinical domain. Specifically, the work deals with a Medical Entity Recognition (MER) on Electronic Health Records (EHRs). Developing a MER system entailed heavy data preproc...

Contextual label sensitive gated network for biomedical event trigger extraction.

Journal of biomedical informatics
Biomedical events play a key role in improving biomedical research. Event trigger identification, extracting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist ...

Ontology mapping for semantically enabled applications.

Drug discovery today
In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential...

Supervised methods to extract clinical events from cardiology reports in Italian.

Journal of biomedical informatics
Clinical narratives are a valuable source of information for both patient care and biomedical research. Given the unstructured nature of medical reports, specific automatic techniques are required to extract relevant entities from such texts. In the ...