medinform.jmir.orgJMI - JMIR Medical Informatics

Sorry, you need to enable javascript for this page to function properly. -- JMIR Publications Menu   Close Menu -- Search User Sign In Sign Up Follow Us on Twitter Home Browse Journal Current Issue Upcoming Issue Browse By Year Browse by Year: Select... 2013 2014 2015 2016 2017 2018 2019 2020 Submit Browse by Issue Browse by Author Browse by Theme Other Journals Journal of Medical Internet Research JMIR Research Protocols JMIR mHealth and uHealth JMIR Mental Health JMIR Public Health and Surveillance Iproceedings Interactive Journal of Medical Research JMIR Formative Research JMIR Human Factors JMIR Serious Games JMIR Medical Education JMIR Cancer JMIR Diabetes JMIR Rehabilitation and Assistive Technologies JMIR Aging JMIR Pediatrics and Parenting JMIR Cardio Journal of Participatory Medicine Medicine 2.0 JMIR Perioperative Medicine JMIR Dermatology JMIR Biomedical Engineering JMIR Nursing JMIR Challenges JMIR Preprints Sign Up Sign In Membership Top Articles Search SUBMIT MEMBERSHIP Follow @jmirpub Advanced Search SIGN IN SIGN UP JMIR Medical Informatics  Journal of Medical Internet Research JMIR Research Protocols JMIR mHealth and uHealth JMIR Mental Health JMIR Public Health and Surveillance Iproceedings Interactive Journal of Medical Research JMIR Formative Research JMIR Human Factors JMIR Serious Games JMIR Medical Education JMIR Cancer JMIR Diabetes JMIR Rehabilitation and Assistive Technologies JMIR Aging JMIR Pediatrics and Parenting JMIR Cardio Journal of Participatory Medicine Medicine 2.0 JMIR Perioperative Medicine JMIR Dermatology JMIR Biomedical Engineering JMIR Nursing JMIR Challenges JMIR Preprints Impact Factor 3.188 Explore our sister journals here. Current Issue Upcoming Issue Top Articles Browse by Year: Select... 2013 2014 2015 2016 2017 2018 2019 2020 Browse: Issues Authors Themes Maintenance Notice Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST . We apologize in advance for any inconvenience this may cause you. Who will be affected? Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central Authors: No access to the submission form or your user account. Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Monday, March 11, 2019 at 4:00 PM . Editors: No access to your user account to assign reviewers or make decisions. Copyeditors: No access to user account. Please download manuscripts you are copyediting before Monday, March 11, 2019 at 4:00 PM . JMIR Medical Informatics Impact Factor 3.188 Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures Now in SCIE and with Impact Factor ! Advertisement What our readers and authors are saying: Featured Theme Issue View Theme View All Themes Read Article This Theme's Latest Article Featured JMIR Sister Journal Current Issue Upcoming Issue Read Article This Theme's Latest Article Latest Submission Open for Peer-Review Adverse Drug Event Detection From Clinical Notes in Electronic Health Records: A Joint Approach for Entity and Relation Extraction Based on Knowledge-aware Neural Attentive Deep Learning Models 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 diagnosis errors, and improve health outcomes. ADEs captured in Electronic Health Records (EHR) structured data, as either coded problems or allergies, are often incomplete leading to underreporting. It is therefore important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain richer documentation of a patient’s adverse drug events. Several natural language processing (NLP) systems were previously proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for automatic extraction of ADEs from clinical notes. Objective: The objective of this study is to improve automatic extraction of ADEs and related information such as drugs and their reason for administration from patient clinical notes. Methods: This research was conducted using discharge summaries from the MIMIC-III database obtained through the National NLP Clinical Challenges (n2c2) annotated with Drugs, drug attributes (Strength, Form, Frequency, Route, Dosage, Duration), Adverse Drug Events, 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 with respect to 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 benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the FAERS database to improve relation extraction, especially when contextual clues are insufficient. Results: Our system achieved new state-of-the-art results on the n2c2 dataset, with significant improvements in recognizing the crucial Drug-->Reason (F1 0.650 vs 0.579) and Drug-->ADE (0.490 vs 0.476) relations. Conclusions: We present a system for extracting drug–centric concepts and relations that outperformed current state-of-the-art results. We show that contextualized embeddings, position-attention mechanism and knowledge graph embeddings effectively improve deep learning–based concept and relation extraction. This study demonstrates the further potential for deep learning–based methods to help extract real world evidence from unstructured patient data for drug safety surveillance. Date Submitted: Feb 25, 2020 Open Peer Review Period: Feb 25, 2020 - Apr 21, 2020 Peer-Review Me Announcement Special Issue: Health Natural Language Processing 2019-09-06 Photo by Markus Spiske on Unsplash Call for Papers With the rapid growth of information technology, the necessity for processing massive amounts of health and medical data utilizing advanced information technologies has also grown. A large amount of valuable data exists in natural text such as free diagnosis text, discharge summaries, online health discussions, eligibility criteria of clinical trials, and so on. Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) language texts. NLP aims to provide computer programs with the ability to process and understand unstructured texts. In the health arena, NLP can assist medical decision making by automatically analyzing the commonalities and differences of large amounts of text data and recommend appropriate actions on behalf of domain experts. NLP techniques have proven to be useful in dealing with the information overload in the health and medical domain, eg, aggregation and summarization of patient notes, treatment analyses, information extraction and retrieval on massive discharge summaries, as well as semantic understanding of patient queries. To that end, the China Conference on Health Information Processing (CHIP) proposes this theme issue, Health Natural Language Processing to contribute to the development of this interdisciplinary research area. CHIP is the annual symposium of the Chinese Information Processing Society of China (CIPS) Techni...