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Link to original content: https://pubmed.ncbi.nlm.nih.gov/33936427/
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. 2021 Jan 25:2020:534-543.
eCollection 2020.

Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing

Daniel R Harris  1   2 , Darren W Henderson  2 , Alexandria Corbeau  2
Affiliations

Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing

Daniel R Harris et al. AMIA Annu Symp Proc. .

Abstract

We present findings on using natural language processing to classify tobacco-related entries from problem lists found within patient's electronic health records. Problem lists describe health-related issues recorded during a patient's medical visit; these problems are typically followed up upon during subsequent visits and are updated for relevance or accuracy. The mechanics of problem lists vary across different electronic health record systems. In general, they either manifest as pre-generated generic problems that may be selected from a master list or as text boxes where a healthcare professional may enter free text describing the problem. Using commonly-available natural language processing tools, we classified tobacco-related problems into three classes: active-user, former-user, and non-user; we further demonstrate that rule-based post-processing may significantly increase precision in identifying these classes (+32%, +22%, +35% respectively). We used these classes to generate tobacco time-spans that reconstruct a patient's tobacco-use history and better support secondary data analysis. We bundle this as an open-source toolkit with flow visualizations indicating how patient tobacco-related behavior changes longitudinally, which can also capture and visualize contradicting information such as smokers being flagged as having never smoked.

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Figures

Figure 1:
Figure 1:
Distinct problem types and instances of problems over time (Kentucky)
Figure 2:
Figure 2:
New problem types introduced over time (Kentucky)
Figure 3:
Figure 3:
A high-level overview of the CLAMP smoking pipeline
Figure 4:
Figure 4:
Visualizing a single patient’s tobacco-related statuses over time (A) and fixing contradictions (B)
Figure 5:
Figure 5:
Confusion matrix for tobacco status classification
Figure 6:
Figure 6:
Capturing patients and their recorded tobacco-use changes over time
See this image and copyright information in PMC

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