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

• URL: http://arxiv.org/abs/2109.00343v1
• Date: Wed, 1 Sep 2021 12:35:26 GMT
• Title: Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts
• Authors: Isabel Segura-Bedmar, David Camino-Perdonas, Sara Guerrero-Aspizua
• Abstract summary: Approximately 300 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. Natural Language Processing (NLP) and Deep Learning can help to extract relevant information to facilitate their diagnosis and treatments.
• Score: 1.6328866317851187
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although rare diseases are characterized by low prevalence, approximately 300 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 diagnosis even more difficult. A delayed diagnosis can negatively affect the patient's life. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) and Deep Learning can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments. The paper explores the use of several deep learning techniques such as Bidirectional Long Short Term Memory (BiLSTM) networks or deep contextualized word representations based on Bidirectional Encoder Representations from Transformers (BERT) to recognize rare diseases and their clinical manifestations (signs and symptoms) in the RareDis corpus. This corpus contains more than 5,000 rare diseases and almost 6,000 clinical manifestations. BioBERT, a domain-specific language representation based on BERT and trained on biomedical corpora, obtains the best results. In particular, this model obtains an F1-score of 85.2% for rare diseases, outperforming all the other models.

Related papers

• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics [24.833696455985795]
  In clinical reality, only few diseases are common, whereas the majority of diseases are less frequent. Current AI models overlook or misclassify these diseases. We propose a deep anomaly detection approach that only requires training data from common diseases to detect also all less frequent diseases.
  arXiv  Detail & Related papers  (2024-06-21T04:59:19Z)
• A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data [28.632437578685842]
  We focus on choroid neoplasias, the most prevalent form of eye cancer in adults, albeit rare with 5.1 per million. Our work introduces a concept-based interpretable model that distinguishes between three types of choroidal tumors, integrating insights from domain experts via radiological reports. Remarkably, this model achieves an F1 score of 0.91, rivaling that of black-box models, but also boosts the diagnostic accuracy of junior doctors by 42%.
  arXiv  Detail & Related papers  (2024-03-08T07:15:53Z)
• RareBench: Can LLMs Serve as Rare Diseases Specialists? [11.828142771893443]
  Generalist Large Language Models (LLMs) have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates. RareBench is a pioneering benchmark designed to evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. We present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians.
  arXiv  Detail & Related papers  (2024-02-09T11:34:16Z)
• Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge [59.323306639144526]
  Many real-world image recognition problems, such as diagnostic medical imaging exams, are emerging. Diagnose is both a long-tailed and multi-label problem, as patients often present with multiple findings. We synthesize common themes, providing recommendations for long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-10-24T18:26:22Z)
• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations [64.91773761529183]
  Primary open-angle glaucoma (POAG) is the leading cause of blindness in the United States. Deep learning has been widely used to detect POAG using fundus images. Human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models.
  arXiv  Detail & Related papers  (2023-01-26T18:53:09Z)
• Subtyping brain diseases from imaging data [3.5849534055078767]
  Clinical neuroscience and cancer imaging have been two areas in which machine learning has offered particular promise. Current chapter focuses on ML methods, especially semi-supervised clustering, that seek disease subtypes. Alzheimer Disease and its prodromal stages, psychosis, depression, autism, and brain cancer are discussed.
  arXiv  Detail & Related papers  (2022-02-16T19:13:53Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection [9.059366200759722]
  In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. We release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients.
  arXiv  Detail & Related papers  (2021-02-16T07:00:49Z)
• Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation [150.52617238140868]
  We propose low-resource medical dialogue generation to transfer the diagnostic experience from source diseases to target ones. We also develop a Graph-Evolving Meta-Learning framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease.
  arXiv  Detail & Related papers  (2020-12-22T13:20:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.