Classification of Use Status for Dietary Supplements in Clinical Notes

Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives

Understanding medication events in clinical narratives is essential to achieving a complete picture of a patient's medication history. While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation. Here, we present a framework to capture multi-dimensional context of medication changes documented in clinical notes. We define specific contextual aspects pertinent to medication change events (i.e. Action, Negation, Temporality, Certainty, and Actor), describe the annotation process and challenges encountered while creating the dataset, and explore models based on state-of-the-art transformers to automate the task. The resulting dataset, Contextualized Medication Event Dataset (CMED), consisting of 9,013 medications annotated over 500 clinical notes, will be released to the community as a shared task in 2021-2022.

Using natural language processing methods to classify use status of dietary supplements in clinical notes

Background

Despite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medications, leading to dangerous clinical outcomes. Electronic health records (EHRs) provide a potential way for active pharmacovigilance on dietary supplements since a fair amount of dietary supplement information, especially those on use status, can be found in clinical notes. Extracting such information is extremely significant for subsequent supplement safety research.

Methods

In this study, we collected 2500 sentences for 25 commonly used dietary supplements and annotated into four classes: Continuing (C), Discontinued (D), Started (S) and Unclassified (U). Both rule-based and machine learning-based classifiers were developed on the same training set and evaluated using the hold-out test set. The performances of the two classifiers were also compared.

Results

The rule-based classifier achieved F-measure of 0.90, 0.85, 0.90, and 0.86 in C, D, S, and U status, respectively. The optimal machine learning-based classifier (Maximum Entropy) achieved F-measure of 0.90, 0.92, 0.91 and 0.88 in C, D, S, and U status, respectively. The comparison result shows that the machine learning-based classifier has a better performance, which is more efficient and scalable especially when the sample size doubles.

Conclusions

Machine learning-based classifier outperforms rule-based classifier in categorization of the use status of dietary supplements in clinical notes. Future work includes applying deep learning methods and developing a hybrid system to approach use status classification task.

Evaluating Automatic Methods to Extract Patients' Supplement Use from Clinical Reports

The widespread prevalence of dietary supplements has drawn extensive attention due to the safety and efficacy issue. Clinical notes document a great amount of detailed information on dietary supplement usage, thus providing a rich source for clinical research on supplement safety surveillance. Identification the use status of dietary supplements is one of the initial steps for the ultimate goal of the supplement safety surveillance. In this study, we built rule-based and machine learning-based classifiers to automatically classify the use status of supplements into four categories: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). In comparison to the machine learning classifier trained on the same datasets, the rule-based classifier showed a better performance with F-measure in the C, D, S, U status of 0.93, 0.98, 0.95, and 0.83, respectively. We further analyzed the errors generated by the rule-based classifier. The classifier can be potentially applied to extract supplement information from clinical notes for supporting research and clinical practice related to patient safety on supplement usage.