ArticleBMC medical informatics and decision making2020
Prediction of blood culture outcome using hybrid neural network model based on electronic health records.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- Machine learning-assisted prediction of pneumonia based on non-invasive measures.Frontiers in public health · 2022Article
- Multi-task learning for Chinese clinical named entity recognition with external knowledge.BMC medical informatics and decision making · 2021Article
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Authors and funding
6 authors.
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Abstract
backgroundBlood cultures are often performed to detect patients who has a serious illness without infections and patients with bloodstream infections. Early positive blood culture prediction is important, as bloodstream infections may cause inflammation of the body, even organ failure or death. However, existing work mainly adopts statistical models with laboratory indicators, and fails to make full use of textual description information from EHRs.
methodsWe study the problem of positive blood culture prediction by using neural network model. Specifically, we first construct dataset from raw EHRs. Then we propose a hybrid neural network which incorporates attention based Bi-directional Long Short-Term Memory and Autoencoder networks to fully capture the information in EHRs.
resultsIn order to evaluate the proposed method, we constructe a dataset which consists of totally 5963 patients who had one or more blood cultures tests during hospitalization. Experimental results show that the proposed neural model gets 91.23% F-measure for this task.
conclusionsThe comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.
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