ArticleBMC medical informatics and decision making2019
Improving rare disease classification using imperfect knowledge graph.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Application of knowledge graphs in rare disease research.Frontiers in public health · 2026Review
- BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature.Computational and structural biotechnology journal · 2024Article
- Article
- Graph Artificial Intelligence in Medicine.Annual review of biomedical data science · 2024Review
- Identification of Smith-Magenis syndrome cases through an experimental evaluation of machine learning methods.Frontiers in computational neuroscience · 2024Article
- Interrelated feature selection from health surveys using domain knowledge graph.Health information science and systems · 2023Article
- [Faster diagnosis of rare diseases with artificial intelligence-A precept of ethics, economy and quality of life].Innere Medizin (Heidelberg, Germany) · 2023Review
- Machine learning in rare disease.Nature methods · 2023Review
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- Improving patient self-description in Chinese online consultation using contextual prompts.BMC medical informatics and decision making · 2022Article
- When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.BMC medical informatics and decision making · 2022Article
- Cognitive Computing-Based CDSS in Medical Practice.Health data science · 2021Review
- KGen: a knowledge graph generator from biomedical scientific literature.BMC medical informatics and decision making · 2020Article
- Phenotypically Similar Rare Disease Identification from an Integrative Knowledge Graph for Data Harmonization: Preliminary Study.JMIR medical informatics · 2020Article
- Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph.Frontiers in genetics · 2020Article
- Editorial: The second international workshop on health natural language processing (HealthNLP 2019).BMC medical informatics and decision making · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundAccurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses a great challenge to machine learning-based approaches. On the other hand, medical knowledge in automatically constructed knowledge graphs (KGs) has the potential to compensate the lack of labeled training examples. This work aims to develop a rare disease classification algorithm that makes effective use of a knowledge graph, even when the graph is imperfect.
methodWe develop a text classification algorithm that represents a document as a combination of a "bag of words" and a "bag of knowledge terms," where a "knowledge term" is a term shared between the document and the subgraph of KG relevant to the disease classification task. We use two Chinese disease diagnosis corpora to evaluate the algorithm. The first one, HaoDaiFu, contains 51,374 chief complaints categorized into 805 diseases. The second data set, ChinaRe, contains 86,663 patient descriptions categorized into 44 disease categories.
resultsOn the two evaluation data sets, the proposed algorithm delivers robust performance and outperforms a wide range of baselines, including resampling, deep learning, and feature selection approaches. Both classification-based metric (macro-averaged F
conclusionMedical knowledge in large-scale knowledge graphs can be effectively leveraged to improve rare diseases classification models, even when the knowledge graph is incomplete.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.