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. 2022 Dec 8;22(24):9609.
doi: 10.3390/s22249609.

Automated Detection of Substance-Use Status and Related Information from Clinical Text

Raid Alzubi  1 , Hadeel Alzoubi  1 , Stamos Katsigiannis  2 , Daune West  3 , Naeem Ramzan  3
Affiliations

Automated Detection of Substance-Use Status and Related Information from Clinical Text

Raid Alzubi et al. Sensors (Basel). .

Abstract

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability.

Keywords: electronic health records; information extraction; machine learning; natural language processing; rule-based systems; substance use.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of using the BRAT annotation tool to annotate three sentences for smoking, alcohol, and drug use, respectively. Screenshot of the BRAT tool’s interface after annotating the three sentences. Using the tool, the parts of the sentence that refer to substance-use type, status, frequency, amount, period, and quit-time have been annotated.
Figure 2
Figure 2
Flowchart of the proposed system for substance-use status and related attribute detection.
See this image and copyright information in PMC

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