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

Fan, Yadan; Zhang, Rui

doi:10.1186/s12911-018-0626-6

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…This, in turn, may lead to overfitting, a modeling error that occurs when a complex model adapts to the idiosyncrasies of the training data and fails to generalize the underlying properties of the problem. Unfortunately, the majority of studies reviewed here were limited to the authors' host institutions [8,10,12,15,17,22,24,25,28,[30][31][32][33]35,40,41,44,66,70,76,79,[84][85][86]89,90,94,95,99,105,106,111,113]. Rarely are such datasets freely accessible to the community.…”

Section: Provenancementioning

confidence: 99%

“…Other types of clinical narratives considered include physician notes [84], progress notes [25,40,90], EHR notes [74,81,116], surgical notes [14,79], and emergency department notes [50,109]. Unspecified type of clinical notes [102] were used mostly for classification [9,12,31,61,86,95,103,113], WSD [33], and disambiguation and IE [36,51,99].…”

Section: Types Of Narrativesmentioning

confidence: 99%

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Clinical Text Data in Machine Learning: Systematic Review

Spasić¹,

Nenadić²

2020

JMIR Med Inform

222

132

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Background Clinical narratives represent the main form of communication within health care, providing a personalized account of patient history and assessments, and offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective The main aim of this study was to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigated the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified 110 relevant studies and extracted information about text data used to support machine learning, NLP tasks supported, and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation, and any relevant statistics. Results The majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents, with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing the predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free-text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable because of the sensitive nature of data considered. Besides the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The majority of studies focused on text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management, and surveillance. Conclusions We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which do not require data annotation.

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Section: Provenancementioning

confidence: 99%

Section: Types Of Narrativesmentioning

confidence: 99%

Clinical Text Data in Machine Learning: Systematic Review

Spasić¹,

Nenadić²

2020

JMIR Med Inform

222

132

View full text Add to dashboard Cite

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“…These should be specialized for the task at hand. For instance, Ben Abdessalem Karaa et al created separate regular expressions to extract causal, preventative, and associative relationships between types of food, genes, and diseases [262], Nikfarjam et al used a list of key phrases to describe patient responses to drugs in social health networks [263], and Fan and Zhang created several regular expressions to extract patient dietary supplement use from clinical notes [264].…”

Section: Computationally Predicted Resourcesmentioning

confidence: 99%

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

et al. 2020

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As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

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“…Jiang et al (2017) develop a model for identifying adverse effects related to dietary supplements as reported by consumers on Twitter, and discover 191 adverse effects pertaining to 4 dietary supplements. Fan et al (2016) and Fan and Zhang (2018) analyze unstructured clinical notes to predict whether a patient started, continued or discontinued a dietary supplement, which can be useful as a building block for identifying adverse effects in clinical notes (as attempted by the same authors in Fan et al (2017) for the drug warfarin). proposes using topic models to analyze the adverse effects of dietary supplements as mentioned in the Dietary Supplement Label Database, and finds that Latent Dirichlet Allocation models (Blei et al, 2003) can be used to group dietary supplements with similar adverse effects based on their labels.…”

Section: Related Workmentioning

confidence: 99%

SUPP.AI: finding evidence for supplement-drug interactions

Wang¹,

Tafjord²,

Cohan³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI, an application for browsing evidence of supplement-drug interactions (SDIs) extracted from the biomedical literature. We train a model to automatically extract supplement information and identify such interactions from the scientific literature. To address the lack of labeled data for SDI identification, we use labels of the closely related task of identifying drug-drug interactions (DDIs) for supervision. We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions. We extract 195k evidence sentences from 22M articles (P=0.82, R=0.58, F1=0.68) for 60k interactions. We create the SUPP.AI application for users to search evidence sentences extracted by our model. SUPP.AI is an attempt to close the information gap on dietary supplements by making upto-date evidence on SDIs more discoverable for researchers, clinicians, and consumers.

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

Cited by 17 publications

References 10 publications

Clinical Text Data in Machine Learning: Systematic Review

Clinical Text Data in Machine Learning: Systematic Review

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

SUPP.AI: finding evidence for supplement-drug interactions

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