AI Medical Compendium Topic:
Language

Clear Filters Showing 891 to 900 of 1291 articles

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: To investigate the clinical utility of deep convolutional neural network (DCNN) tract classification as a new imaging tool in the preoperative evaluation of children with focal epilepsy (FE).

Comparing Different Methods for Named Entity Recognition in Portuguese Neurology Text.

Journal of medical systems
Electronic Medical Records (EMRs) are written in an unstructured way, often using natural language. Information Extraction (IE) may be used for acquiring knowledge from such texts, including the automatic recognition of meaningful entities, through m...

Research on Chinese medical named entity recognition based on collaborative cooperation of multiple neural network models.

Journal of biomedical informatics
Medical named entity recognition (NER) in Chinese electronic medical records (CEMRs) has drawn much research attention, and plays a vital prerequisite role for extracting high-value medical information. In 2018, China Health Information Processing Co...

Multiple features for clinical relation extraction: A machine learning approach.

Journal of biomedical informatics
Relation extraction aims to discover relational facts about entity mentions from plain texts. In this work, we focus on clinical relation extraction; namely, given a medical record with mentions of drugs and their attributes, we identify relations be...

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks.

Journal of chemical information and modeling
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated, unsupervised method ...

Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition.

Journal of biomedical informatics
With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequence-l...

Identifying epilepsy psychiatric comorbidities with machine learning.

Acta neurologica Scandinavica
OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classif...

Improving clinical named entity recognition in Chinese using the graphical and phonetic feature.

BMC medical informatics and decision making
BACKGROUND: Clinical Named Entity Recognition is to find the name of diseases, body parts and other related terms from the given text. Because Chinese language is quite different with English language, the machine cannot simply get the graphical and ...

Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches.

BMC medical informatics and decision making
BACKGROUND: Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigat...

Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms f...