AIMC Journal:
Journal of biomedical informatics

Showing 371 to 380 of 650 articles

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.

Journal of biomedical informatics
OBJECTIVE: There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In th...

Extracting drug-drug interactions with hybrid bidirectional gated recurrent unit and graph convolutional network.

Journal of biomedical informatics
Drug-drug interactions are critical in studying drug side effects. Thus, quickly and accurately identifying the relationship between drugs is necessary. Current methods for biomedical relation extraction include only the sequential information of sen...

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records.

Journal of biomedical informatics
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little acc...

Neural network-based approaches for biomedical relation classification: A review.

Journal of biomedical informatics
The explosive growth of biomedical literature has created a rich source of knowledge, such as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in unstructured free text. Biomedical relation classification aims to ...

Wikidata: A large-scale collaborative ontological medical database.

Journal of biomedical informatics
Created in October 2012, Wikidata is a large-scale, human-readable, machine-readable, multilingual, multidisciplinary, centralized, editable, structured, and linked knowledge-base with an increasing diversity of use cases. Here, we raise awareness of...

Adversarial training based lattice LSTM for Chinese clinical named entity recognition.

Journal of biomedical informatics
Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used to Chines...

A two-stage deep learning approach for extracting entities and relationships from medical texts.

Journal of biomedical informatics
This work presents a two-stage deep learning system for Named Entity Recognition (NER) and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many natural language understanding applications in the biomedical domain. Autom...

Chinese clinical named entity recognition with radical-level feature and self-attention mechanism.

Journal of biomedical informatics
Named entity recognition is a fundamental and crucial task in medical natural language processing problems. In medical fields, Chinese clinical named entity recognition identifies boundaries and types of medical entities from unstructured text such a...

Towards a characterization of apparent contradictions in the biomedical literature using context analysis.

Journal of biomedical informatics
BACKGROUND: With the substantial growth in the biomedical research literature, a larger number of claims are published daily, some of which seemingly disagree with or contradict prior claims on the same topics. Resolving such contradictions is critic...

Active deep learning for the identification of concepts and relations in electroencephalography reports.

Journal of biomedical informatics
The identification of medical concepts, their attributes and the relations between concepts in a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. However, th...