A convolutional route to abbreviation disambiguation in clinical text

Joopudi, Venkata; Dandala, Bharath; Devarakonda, Murthy V.

doi:10.1016/j.jbi.2018.07.025

Cited by 34 publications

(39 citation statements)

References 11 publications

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“…To address some of the findings from the error analysis, we plan to leverage our clinical abbreviation expansion components [ 86 ] to help resolve ambiguous mentions and also incorporate assertion recognition [ 26 ] to capture the belief state of the physician on a concept (negated, hypothetical, conditional).…”

Section: Discussionmentioning

confidence: 99%

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Dandala¹,

Joopudi²,

Tsou³

et al. 2020

JMIR Med Inform

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Background An adverse drug event (ADE) is commonly defined as “an injury resulting from medical intervention related to a drug.” Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain a richer documentation of a patient’s ADE. Several natural language processing (NLP) systems have been proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for the automatic extraction of ADEs from clinical notes. Objective This study aims to improve the automatic extraction of ADEs and related information such as drugs, their attributes, and reason for administration from the clinical notes of patients. Methods This research was conducted using discharge summaries from the Medical Information Mart for Intensive Care III (MIMIC-III) database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with drugs, drug attributes (ie, strength, form, frequency, route, dosage, duration), ADEs, reasons, and relations between drugs and other entities. We developed a deep learning–based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations for each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified the benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the US Food and Drug Administration Adverse Event Reporting System database to improve relation extraction, especially when contextual clues were insufficient. Results Our system achieved new state-of-the-art results on the n2c2 data set, with significant improvements in recognizing crucial drug−reason (F1=0.650 versus F1=0.579) and drug−ADE (F1=0.490 versus F1=0.476) relations. Conclusions This study presents a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results and shows that contextualized embeddings, position-attention mechanisms, and knowledge graph embeddings effectively improve deep learning–based concepts and relation extraction. This study demonstrates the potential for deep learning–based methods to help extract real-world evidence from unstructured patient data for drug safety surveillance.

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Section: Discussionmentioning

confidence: 99%

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Dandala¹,

Joopudi²,

Tsou³

et al. 2020

JMIR Med Inform

Self Cite

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“…As a matter of fact, a challenging aspect of NLP in medicine is the disambiguation of abbreviations. Joopudi et al (40) trained a Convolutional Neural Network (CNN) to disambiguate abbreviation senses. For example, mg could have two senses: myasthenia gravis and milligrams.…”

Section: Nlp Applications In Clinical Contextmentioning

confidence: 99%

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

et al. 2019

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Clinical practice requires the production of a time-and resource-consuming great amount of notes. They contain relevant information, but their secondary use is almost impossible, due to their unstructured nature. Researchers are trying to address this problems, with traditional and promising novel techniques. Application in real hospital settings seems not to be possible yet, though, both because of relatively small and dirty dataset, and for the lack of language-specific pre-trained models. Aim: Our aim is to demonstrate the potential of the above techniques, but also raise awareness of the still open challenges that the scientific communities of IT and medical practitioners must jointly address to realize the full potential of unstructured content that is daily produced and digitized in hospital settings, both to improve its data quality and leverage the insights from data-driven predictive models. Methods: To this extent, we present a narrative literature review of the most recent and relevant contributions to leverage the application of Natural Language Processing techniques to the free-text content electronic patient records. In particular, we focused on four selected application domains, namely: data quality, information extraction, sentiment analysis and predictive models, and automated patient cohort selection. Then, we will present a few empirical studies that we undertook at a major teaching hospital specializing in musculoskeletal diseases. Results: We provide the reader with some simple and affordable pipelines, which demonstrate the feasibility of reaching literature performance levels with a single institution non-English dataset. In such a way, we bridged literature and real world needs, performing a step further toward the revival of notes fields.

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“…In contrast to the techniques employed in the abovementioned studies, deep learning methods [25,26] have the obvious advantage that feature engineering can be avoided. In [26], Ahmed et al suggested a deep learning model to train a vector for the context of each sense of each abbreviation.…”

Section: Asdmentioning

confidence: 99%

“…For disambiguation, the cosine-similarities for the context vector of the sentence with the context vector of each sense are calculated, and the sense with the maximum value is selected. In [25], Joopudi et al proposed a convolutional neural network with one convolutional kernel, a max-pooling layer, and a fully connected feed-forward neural network layer followed by a fully connected softmax classifier. Except for the embedding of surrounding words, the location and part of speech information of the word are considered in the context.…”

Section: Asdmentioning

confidence: 99%

Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis

Zhu

et al. 2019

Future Generation Computer Systems

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A convolutional route to abbreviation disambiguation in clinical text

Cited by 34 publications

References 11 publications

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis

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