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Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network
Medication name inference is the task of mapping user friendly medication names from a free-form text to a concept in a normalized medication list.
LATTE: Latent Type Modeling for Biomedical Entity Linking
This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS).
Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service
Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models.
Dynamic Transfer Learning for Named Entity Recognition
We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture.
Joint Entity Extraction and Assertion Detection for Clinical Text
Most of the existing systems treat this task as a pipeline of two separate tasks, i. e., named entity recognition (NER) and rule-based negation detection.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data.
