Article

Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts

2022-04-28

Abstract excerpt

<h4>Background: </h4> and Objective Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to identify them. In addition to this, rare diseases usually show a wide variety of manifestations, which might make the d...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a51d8fb9-c403-567a-9ff3-cf412c7875b8
DOI: 10.21203/rs.3.rs-1569369/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from textsDOI 10.21203/rs.3.rs-1569369/v1Read

Deep Learning for Rare Disease: A Scoping ReviewSemantic proximity Read

A systematic review and meta-analysis of the Diagnostic Performance of Large Language Models in Rare DiseasesSemantic proximity Read

A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systemsSemantic proximity Read

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning-Based Method to Predict Crucial Complication Phenotypes.Semantic proximity Read

Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 CasesSemantic proximity Read

DLNDD: An Explainable Deep Learning Framework for the Early Detection and Classification of Rare DiseasesSemantic proximity Read

Pre-diagnosis Diagnosis Treatment (PDT): A Novel Architecture for Diagnosis and Treatment-option Generation in Rare Castleman Diseases based on GPT4oSemantic proximity Read

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

Language Model Applications for Early Diagnosis of Childhood EpilepsySemantic proximity Read

NER Sequence Embedding of Unified Medical Corpora to Incorporate Semantic Intelligence in Big Data Healthcare DiagnosticsSemantic proximity Read

A hybrid framework with large language models for rare disease phenotyping.Semantic proximity Read

NER Sequence Embedding of Unified Medical Corpora to Incorporate Semantic Intelligence in Big Data Healthcare DiagnosticsSemantic proximity Read