Abstract:
BACKGROUND:Blood 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. METHODS:We 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. RESULTS:In 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. CONCLUSIONS:The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.
journal_name
BMC Med Inform Decis Makjournal_title
BMC medical informatics and decision makingauthors
Cheng M,Zhao X,Ding X,Gao J,Xiong S,Ren Ydoi
10.1186/s12911-020-1113-4subject
Has Abstractpub_date
2020-07-09 00:00:00pages
121issue
Suppl 3issn
1472-6947pii
10.1186/s12911-020-1113-4journal_volume
20pub_type
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journal_title:BMC medical informatics and decision making
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