2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records

Henry, Sam; Buchan, Kevin; Filannino, Michele; Stubbs, Amber; Uzuner, Özlem

doi:10.1093/jamia/ocz166

Cited by 224 publications

(162 citation statements)

References 22 publications

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“…Hence, Medical Information Extraction in the Age of Deep Learning Table 4 Medical Named Entity Recognition: Medication Attributes. Benchmark Datasets: n2c2 [56]; i2b2 2009 [57]; MADE 1.0 [59]; DDI [60].…”

Section: Citationsmentioning

confidence: 99%

“…The top performers for the medication attribute REX task [62] employed a joint learning approach based on CNN-RNN Medical Information Extraction in the Age of Deep Learning Table 5 Medical Relation Extraction: Medication-Attribute Relations (including ADEs). Benchmark Datasets: n2c2 [56]; MADE 1.0 [59].…”

Section: Medication-attribute Relationsmentioning

confidence: 99%

“…For medication information, four gold standards had a great impact on the field in the past years. The most recent one came out of the 2018 n2c2 Shared Task on Adverse Drug Events and Medication Extraction in Electronic Health Records 56 , a successor of the 2009 i2b2 Medication Challenge 57 , now with a focus on Adverse Drug Events (ADEs). It includes 505 discharge summaries (303 in the training set and 202 in the test set), which originate from the MIMIC-III clinical care database 58 .…”

Section: Deep Neural Network For Medical Information Extractionmentioning

confidence: 99%

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Medical Information Extraction in the Age of Deep Learning

Hahn

Oleynik

2020

Yearb Med Inform

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Objectives: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes—diseases and drugs (or medications)—and relations between them. Methods: For the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence. Results: In the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies. Conclusions: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.

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Section: Citationsmentioning

confidence: 99%

Section: Medication-attribute Relationsmentioning

confidence: 99%

Section: Deep Neural Network For Medical Information Extractionmentioning

confidence: 99%

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Medical Information Extraction in the Age of Deep Learning

Hahn

Oleynik

2020

Yearb Med Inform

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“…Furthermore, the clinical natural language processing (NLP) community has organized several open challenges such as the 2010 Informatics for Integrating Biology & the Bedside/Veterans Affairs NLP Challenge [ 26 ], Text Analysis Conference 2017 Adverse Drug Reactions Track [ 27 ], and BioCreative V Chemical Disease Relation task [ 28 ]. Recently, 2 such challenges, Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) [ 29 ] and the 2018 National NLP Clinical Challenges (n2c2) Shared Task Track 2 [ 30 ], were organized to extract drugs , drug attributes, ADEs , reasons for prescribing drugs, and their relations from clinical notes. The results from these 2 challenges showed that deep learning techniques outperform traditional machine learning techniques for this task, and significant improvement is still required for drug−{ADE, reason} relation extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies employed traditional machine learning techniques [ 31 - 34 ], such as conditional random fields (CRF) [ 35 ] for NER and support vector machines [ 36 ] for relation classification. Several recent approaches [ 37 - 44 ], developed on MADE 1.0 [ 29 ] and 2018 n2c2 Shared Task Track 2 [ 30 ] data sets, employed deep learning techniques, such as bidirectional, long short-term memory–conditional random fields (BiLSTM-CRFs) [ 45 ], for NER and convolutional neural network (CNN) [ 46 ] for relation classification, and showed numerous advantages resulting in better performance and less feature engineering. However, there is an inevitable error propagation issue with pipeline-based methods because of the following:…”

Section: Introductionmentioning

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.