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BMC Med Inform Decis Mak. 2019 Aug 19;19(1):164. doi: 10.1186/s12911-019-0894-9.

Automated detection of altered mental status in emergency department clinical notes: a deep learning approach.

Author information

1
Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA. jobeid@musc.edu.
2
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA. jobeid@musc.edu.
3
Department of Clinical Pharmacy and Outcome Sciences, Medical University of South Carolina, Charleston, SC, USA.
4
Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC, USA.
5
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.
6
Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
7
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

Abstract

BACKGROUND:

Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. In an effort to automate the identification of altered mental status (AMS) in emergency department provider notes for the purpose of decision support, we compare the performance of classic bag-of-words-based machine learning classifiers and novel deep learning approaches.

METHODS:

We used a case-control study design to extract an adequate number of clinical notes with AMS and non-AMS based on ICD codes. The notes were parsed to extract the history of present illness, which was used as the clinical text for the classifiers. The notes were manually labeled by clinicians. As a baseline for comparison, we tested several traditional bag-of-words based classifiers. We then tested several deep learning models using a convolutional neural network architecture with three different types of word embeddings, a pre-trained word2vec model and two models without pre-training but with different word embedding dimensions.

RESULTS:

We evaluated the models on 1130 labeled notes from the emergency department. The deep learning models had the best overall performance with an area under the ROC curve of 98.5% and an accuracy of 94.5%. Pre-training word embeddings on the unlabeled corpus reduced training iterations and had performance that was statistically no different than the other deep learning models.

CONCLUSION:

This supervised deep learning approach performs exceedingly well for the detection of AMS symptoms in clinical text in our environment. Further work is needed for the generalizability of these findings, including evaluation of these models in other types of clinical notes and other environments. The results seem promising for the ultimate use of these types of classifiers in combination with other information derived from the electronic health records as input for clinical decision support.

KEYWORDS:

Altered mental status; Decision support; Deep learning; Machine learning; Pulmonary embolism; Word embedding

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