Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Combining deep learning with token selection for patient phenotyping from electronic health records.

Scientific reports - 29 Jan 2020

Yang Zhen, Dehmer Matthias, Yli-Harja Olli, Emmert-Streib Frank

Abstract excerpt

Artificial intelligence provides the opportunity to reveal important information buried in large amounts of complex data. Electronic health records (eHRs) are a source of such big data that provide a multitude of health related clinical information about patients. However, text data from eHRs, e.g., discharge summary notes, are challenging in their analysis because these notes are free-form texts and the writing...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Deep Learning
Share to Artificial Intelligence
Share to Biostatistics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Chronic Pain

Share to Computational Biology

Share to Electronic Health Records

Share to Machine Learning

Share to Neural Networks, Computer

Source

Open in PubMed Open full text

Identifiers

PMID: 31996705
DOI: 10.1038/s41598-020-58178-1
PMCID: PMC6989657

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Combining deep learning with token selection for patient phenotyping from electronic health records.PMID 31996705Read ↗

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.Semantic proximity Read ↗

Empirical Findings on the Role of Structured Data, Unstructured Data, and their Combination for Automatic Clinical Phenotyping.Semantic proximity Read ↗

Generalist Foundation Models Are Not Clinical Enough for Hospital OperationsSemantic proximity Read ↗

Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification.Semantic proximity Read ↗

Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomesSemantic proximity Read ↗

Comparing natural language processing representations of disease sequences for prediction in the electronic healthcare recordSemantic proximity Read ↗

Developing a natural language processing system using transformer-based models for adverse drug event detection in electronic health recordsSemantic proximity Read ↗

Evaluation of T2DM Phenotyping Using Optimized Retrieval-Augmented Generation (RAG) and the Impact of Embedding Model, Context, and PromptSemantic proximity Read ↗

clickBrick Prompt Engineering: Optimizing Large Language Model Performance in Clinical PsychiatrySemantic proximity Read ↗

Text Classification of German Mental Healthcare Data: A Hands-on Tutorial on Natural Language Processing Techniques.Semantic proximity Read ↗

PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology.Semantic proximity Read ↗

Building a Best-in-Class Automated De-identification Tool for Electronic Health Records Through Ensemble LearningSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.