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. 2023 Jul:143:104391.
doi: 10.1016/j.jbi.2023.104391. Epub 2023 May 15.

A deep learning approach for medication disposition and corresponding attributes extraction

Qiwei Gan  1 , Mengke Hu  1 , Kelly S Peterson  2 , Hannah Eyre  1 , Patrick R Alba  1 , Annie E Bowles  1 , Johnathan C Stanley  1 , Scott L DuVall  1 , Jianlin Shi  3
Affiliations

A deep learning approach for medication disposition and corresponding attributes extraction

Qiwei Gan et al. J Biomed Inform. 2023 Jul.

Abstract

Objective: This article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task.

Methods: The dataset was prepared using Contextualized Medication Event Dataset (CMED), including 500 notes from 296 patients. Our system consisted of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC). These three components were built using transformer models with slightly different architecture and input text engineering. A zero-shot learning solution for CC was also explored.

Results: Our best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively.

Conclusion: In this study, we implemented a deep learning-based NLP system and demonstrated that our approach of (1) utilizing special tokens helps our model to distinguish multiple medications mentions in the same context; (2) aggregating multiple events of a single medication into multiple labels improves our model's performance.

Keywords: Clinical natural language processing; Concept-attribute relation classification; Medication information extraction.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1.
Figure 1.
Aggregating multiple events of the same medication
Figure 2.
Figure 2.
Dissemble multiple events of the same medication.
See this image and copyright information in PMC

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