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Objective: We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.Materials and Methods: Two tasks were performed in this study. For the named entity recognition (NER) task, Bi-LSTM-CRF (bidirectional long short-term memory conditional random field) and BERT (bidirectional encoder representations from transformers) models were trained and compared with CRF model as a baseline to recognize the named entities of DSs and events from clinical notes. In the relation extraction (RE) task, 2 deep learning models, including attention-based Bi-LSTM and convolutional neural network as well as a random forest model were trained to extract the relations between DSs and events, which were categorized into 3 classes: positive (ie, indication), negative (ie, adverse events), and not related. The best performed NER and RE models were further applied on clinical notes mentioning 88 DSs for discovering DSs adverse events and indications, which were compared with a DS knowledge base.Results: For the NER task, deep learning models achieved a better performance than CRF, with F1 scores above 0.860. The attention-based Bi-LSTM model performed the best in the RE task, with an F1 score of 0.893. When comparing DS event pairs generated by the deep learning models with the knowledge base for DSs and event, we found both known and unknown pairs.Conclusions: Deep learning models can detect adverse events and indication of DSs in clinical notes, which hold great potential for monitoring the safety of DS use. [ABSTRACT FROM AUTHOR]

The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system. [ABSTRACT FROM AUTHOR]

Cisplatin-based chemotherapy is the standard treatment for metastatic ovarian cancer (OC). However, chemoresistance continues to pose significant clinical challenges. Recent research has highlighted the baculoviral inhibitor of the apoptosis protein repeat-containing 5 (BIRC5) as a member of the inhibitor of the apoptosis protein (IAP) family. Notably, BIRC5, which has robust anti-apoptotic capabilities, is overexpressed in numerous cancers. Its dysfunction has been linked to challenges in cancer treatment. Yet, the role of BIRC5 in the chemoresistance of OC remains elusive. In our present study, we observed an upregulation of BIRC5 in cisplatin-resistant cell lines. This upregulation was associated with enhanced chemoresistance, which was diminished when the expression of BIRC5 was silenced. Intriguingly, BIRC5 exhibited a high number of N6-methyladenosine (m 6 A) binding sites. The modification of m 6 A was found to enhance the expression of BIRC5 by recognizing and binding to the 3'-UTR of mRNA. Additionally, the insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was shown to stabilize BIRC5 mRNA, synergizing with METTL3 and intensifying chemoresistance. Supporting these in vitro findings, our in vivo experiments revealed that tumors were significantly smaller in size and volume when BIRC5 was silenced. This reduction was notably counteracted by co-silencing BIRC5 and overexpressing IGF2BP1. Our results underscored the pivotal role of BIRC5 in chemoresistance. The regulation of its expression and the stability of its mRNA were influenced by m 6 A modifications involving both METTL3 and IGF2BP1. These insights presented BIRC5 as a promising potential therapeutic target for addressing cisplatin resistance in OC., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Concept encoding, which maps text spans to concepts in standard terminologies, is a critical component in clinical natural language processing (NLP) systems to allow semantic interoperability with other clinical applications. A majority of clinical NLP systems adopt dictionary or lexicon based approaches and the performance of concept encoding is often evaluated using a human created gold standard generated with reference to the most up-to-date standard terminologies available at the time of gold standard creation. With the advance of medical science, standard terminologies or dictionaries can evolve. However, it remains unknown whether the dictionary updates will impact the performance of concept encoding. In this study, we evaluated the annotation performance of two clinical NLP systems, cTAKES and MedXN based on updated dictionaries to gain further insights. Specifically, we compared the automatic annotation results with previously manually generated gold standards. The results of our study demonstrate the annotation changes based on dictionary updates in clinical NLP systems and that it is necessary to do temporal management for gold standards, which raises the need for appropriate terminology management tools for back version compatibility to update gold standards.

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.

Clinical notes contain rich information about supplement use that is critical for detecting adverse interactions between supplements and prescribed medications. It is important to know the context in which supplements are mentioned in clinical notes to be able to correctly identify patients that either currently take the supplement or did so in the past. We applied text mining methods to automatically classify supplement use into four status categories: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). We manually classified 1,300 sentences into these categories, which were further split as training (1000 sentences) and testing (300 sentences) sets. We evaluated the 7 types of feature sets and 5 algorithms, and the best model (SVM with unigram, bigram and indicator word within certain distance) performed F-measure of 0.906, 0.913, 0.914, 0.715 for status C, D, S, U, respectively on the testing set. This study demonstrates the feasibility of using text mining methods to classify supplement use status from clinical notes.

Clinical notes contain rich information about dietary supplements, which are critical for detecting signals of dietary supplement side effects and interactions between drugs and supplements. One of the important factors of supplement documentation is usage status, such as started and discontinuation. Such information is usually stored in the unstructured clinical notes. We developed a rule-based classifier to identify supplement usage status in clinical notes. The categories referring to the patient's status of supplement use were classified into four classes: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). Clinical notes containing 10 of the most commonly consumed supplements (i.e., alfalfa, echinacea, fish oil, garlic, ginger, ginkgo, ginseng, melatonin, St. John's Wort, and Vitamin E) were retrieved from the University of Minnesota Clinical Data Repository. The gold standard was defined by manually annotating 1000 randomly selected sentences or statements mentioning at least one of these 10 supplements. The rules in the classifier was initially developed on two-thirds of the set of 7 supplements (i.e., alfalfa, garlic, ginger, ginkgo, ginseng, St. John's Wort, and Vitamin E); the performance was evaluated on the remaining one-third of this set. To evaluate the generalizability of rules, we further validated the second testing set on other 3 supplements (i.e., echinacea, fish oil, and melatonin). The performance of the classifier achieved F-measures of 0.95, 0.97, 0.96, and 0.96 for status C, D, S, and U on 7 supplements, respectively. The classifier also showed good generalizability when it was applied to the other 3 supplements with F-measures of 0.96 for C, 0.96 for D, 0.95 for S, and 0.89 for U. This study demonstrated that the classifier can accurately classify supplement usage status, which can be further integrated as a module into the existing natural language processing pipeline for supporting dietary supplement knowledge discovery.