ArticleHealth information science and systems2019
Neural networks for mining the associations between diseases and symptoms in clinical notes.
Article in Health information science and systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Review
- Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh.PLoS neglected tropical diseases · 2025Article
- Seeing Your Stories: Visualization for Narrative Medicine.Health data science · 2024Article
- Visualization of medical concepts represented using word embeddings: a scoping review.BMC medical informatics and decision making · 2022Article
- Article
- Constructing a knowledge-based heterogeneous information graph for medical health status classification.Health information science and systems · 2020Article
- Classification and prediction of diabetes disease using machine learning paradigm.Health information science and systems · 2020Article
- Guest Editorial: Special issue on "Application of artificial intelligence in health research".Health information science and systems · 2020Article
- Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.Journal of diabetes research · 2020Article
- Words prediction based on N-gram model for free-text entry in electronic health records.Health information science and systems · 2019Article
- Extracting features with medical sentiment lexicon and position encoding for drug reviews.Health information science and systems · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are challenges for analyzing the narrative clinical notes in Electronic Health Records (EHRs) because of their unstructured nature. Mining the associations between the clinical concepts within the clinical notes can support physicians in making decisions, and provide researchers evidence about disease development and treatment. In this paper, in order to model and analyze disease and symptom relationships in the clinical notes, we present a concept association mining framework that is based on word embedding learned through neural networks. The approach is tested using 154,738 clinical notes from 500 patients, which are extracted from the Indiana University Health's Electronic Health Records system. All patients are diagnosed with more than one type of disease. The results show that this concept association mining framework can identify related diseases and symptoms. We also propose a method to visualize a patients' diseases and related symptoms in chronological order. This visualization can provide physicians an overview of the medical history of a patient and support decision making. The presented approach can also be expanded to analyze the associations of other clinical concepts, such as social history, family history, medications, etc.
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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.