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"Natural Language Processing"

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https://www.readbyqxmd.com/read/28643174/natural-language-processing-for-ehr-based-pharmacovigilance-a-structured-review
#1
REVIEW
Yuan Luo, William K Thompson, Timothy M Herr, Zexian Zeng, Mark A Berendsen, Siddhartha R Jonnalagadda, Matthew B Carson, Justin Starren
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions...
June 22, 2017: Drug Safety: An International Journal of Medical Toxicology and Drug Experience
https://www.readbyqxmd.com/read/28639832/comparing-diagnostic-performance-of-digital-breast-tomosynthesis-and-full-field-digital-mammography-in-a-hybrid-screening-environment
#2
Catherine S Giess, Sarvenaz Pourjabbar, Ivan K Ip, Ronilda Lacson, Emily Alper, Ramin Khorasani
OBJECTIVE: The purpose of this study is to compare the diagnostic performance of screening digital breast tomosynthesis (DBT) to that of full-field digital mammography (FFDM) in a mixed DBT and FFDM imaging environment. MATERIALS AND METHODS: This retrospective observational study consisted of all female patients undergoing screening DBT or FFDM at an academic medical center and outpatient imaging facility between October 2012 and May 2015. Patient demographics and personal history of breast cancer were collected from the electronic medical record...
June 22, 2017: AJR. American Journal of Roentgenology
https://www.readbyqxmd.com/read/28634427/leveraging-food-and-drug-administration-adverse-event-reports-for-the-automated-monitoring-of-electronic-health-records-in-a-pediatric-hospital
#3
Huaxiu Tang, Imre Solti, Eric Kirkendall, Haijun Zhai, Todd Lingren, Jaroslaw Meller, Yizhao Ni
The objective of this study was to determine whether the Food and Drug Administration's Adverse Event Reporting System (FAERS) data set could serve as the basis of automated electronic health record (EHR) monitoring for the adverse drug reaction (ADR) subset of adverse drug events. We retrospectively collected EHR entries for 71 909 pediatric inpatient visits at Cincinnati Children's Hospital Medical Center. Natural language processing (NLP) techniques were used to identify positive diseases/disorders and signs/symptoms (DDSSs) from the patients' clinical narratives...
2017: Biomedical Informatics Insights
https://www.readbyqxmd.com/read/28634156/classifying-chinese-questions-related-to-health-care-posted-by-consumers-via-the-internet
#4
Haihong Guo, Xu Na, Li Hou, Jiao Li
BACKGROUND: In question answering (QA) system development, question classification is crucial for identifying information needs and improving the accuracy of returned answers. Although the questions are domain-specific, they are asked by non-professionals, making the question classification task more challenging. OBJECTIVE: This study aimed to classify health care-related questions posted by the general public (Chinese speakers) on the Internet. METHODS: A topic-based classification schema for health-related questions was built by manually annotating randomly selected questions...
June 20, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28634104/natural-language-processing-for-asthma-ascertainment-in-different-practice-settings
#5
Chung-Il Wi, Sunghwan Sohn, Mir Ali, Elizabeth Krusemark, Euijung Ryu, Hongfang Liu, Young J Juhn
BACKGROUND: We developed and validated NLP-PAC, a natural language processing (NLP) algorithm based on predetermined asthma criteria (PAC) for asthma ascertainment using electronic health records at Mayo Clinic. OBJECTIVE: To adapt NLP-PAC in a different health care setting, Sanford Children Hospital, by assessing its external validity. METHODS: The study was designed as a retrospective cohort study that used a random sample of 2011-2012 Sanford Birth cohort (n = 595)...
June 17, 2017: Journal of Allergy and Clinical Immunology in Practice
https://www.readbyqxmd.com/read/28630032/what-are-people-tweeting-about-zika-an-exploratory-study-concerning-its-symptoms-treatment-transmission-and-prevention
#6
Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William Romine, Amit Sheth
BACKGROUND: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus. OBJECTIVE: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment...
June 19, 2017: JMIR Public Health and Surveillance
https://www.readbyqxmd.com/read/28627690/data-mining-and-pathway-analysis-of-glucose-6-phosphate-dehydrogenase-with-natural-language-processing
#7
Long Chen, Chunhua Zhang, Yanling Wang, Yuqian Li, Qiaoqiao Han, Huixin Yang, Yuechun Zhu
Human glucose-6-phosphate dehydrogenase (G6PD) is a crucial enzyme in the pentose phosphate pathway, and serves an important role in biosynthesis and the redox balance. G6PD deficiency is a major cause of neonatal jaundice and acute hemolyticanemia, and recently, G6PD has been associated with diseases including inflammation and cancer. The aim of the present study was to conduct a search of the National Center for Biotechnology Information PubMed library for articles discussing G6PD. Genes that were identified to be associated with G6PD were recorded, and the frequency at which each gene appeared was calculated...
June 15, 2017: Molecular Medicine Reports
https://www.readbyqxmd.com/read/28624642/drugsemantics-a-corpus-for-named-entity-recognition-in-spanish-summaries-of-product-characteristics
#8
Isabel Moreno, Ester Boldrini, Paloma Moreda, M Teresa Romá-Ferri
For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care. The problem is that such information is stored in different sources and their consultation time is limited. In this context, Natural Language Processing techniques can be applied to efficiently transform textual data into structured information so that it could be used in critical healthcare applications, being of help for physicians in their daily workload, such as: decision support systems, cohort identification, patient management, etc...
June 14, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28622808/characterizing-and-predicting-rates-of-delirium-across-general-hospital-settings
#9
Thomas H McCoy, Kamber L Hart, Roy H Perlis
OBJECTIVE: To better understand variation in reported rates of delirium, this study characterized delirium occurrence rate by department of service and primary admitting diagnosis. METHOD: Nine consecutive years (2005-2013) of general hospital admissions (N=831,348) were identified across two academic medical centers using electronic health records. The primary admitting diagnosis and the treating clinical department were used to calculate occurrence rates of a previously published delirium definition composed of billing codes and natural language processing of discharge summaries...
May 2017: General Hospital Psychiatry
https://www.readbyqxmd.com/read/28614702/de-identification-of-psychiatric-intake-records-overview-of-2016-cegs-n-grid-shared-tasks-track-1
#10
Amber Stubbs, Michele Filannino, Özlem Uzuner
The 2016 CEGS N-GRID shared tasks for clinical records contained three tracks. Track 1 focused on de-identification of a new corpus of 1,000 psychiatric intake records. This track tackled de-identification in two sub-tracks: Track 1.A was a "sight unseen" task, where nine teams ran existing de-identification systems, without any modifications or training, on 600 new records in order to gauge how well systems generalize to new data. The best-performing system for this track scored an F1 of 0.799. Track 1.B was a traditional Natural Language Processing (NLP) shared task on de-identification, where 15 teams had two months to train their systems on the new data, then test it on an unannotated test set...
June 11, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28611016/how-do-you-relax-when-you-re-stressed-a-content-analysis-and-infodemiology-study-of-stress-related-tweets
#11
Son Doan, Amanda Ritchart, Nicholas Perry, Juan D Chaparro, Mike Conway
BACKGROUND: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Twitter is a microblog platform that allows users to post their own personal messages (tweets), including their expressions about feelings and actions related to stress and stress management (eg, relaxing)...
June 13, 2017: JMIR Public Health and Surveillance
https://www.readbyqxmd.com/read/28606869/predicting-mental-conditions-based-on-history-of-present-illness-in-psychiatric-notes-with-deep-neural-networks
#12
Tung Tran, Ramakanth Kavuluru
BACKGROUND: Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task. OBJECTIVE: We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient's history of present illness typically occurring in the beginning of a psychiatric initial evaluation note...
June 9, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28606104/unlocking-echocardiogram-measurements-for-heart-disease-research-through-natural-language-processing
#13
Olga V Patterson, Matthew S Freiberg, Melissa Skanderson, Samah J Fodeh, Cynthia A Brandt, Scott L DuVall
BACKGROUND: In order to investigate the mechanisms of cardiovascular disease in HIV infected and uninfected patients, an analysis of echocardiogram reports is required for a large longitudinal multi-center study. IMPLEMENTATION: A natural language processing system using a dictionary lookup, rules, and patterns was developed to extract heart function measurements that are typically recorded in echocardiogram reports as measurement-value pairs. Curated semantic bootstrapping was used to create a custom dictionary that extends existing terminologies based on terms that actually appear in the medical record...
June 12, 2017: BMC Cardiovascular Disorders
https://www.readbyqxmd.com/read/28605776/exploring-convolutional-neural-networks-for-drug-drug-interaction-extraction
#14
Víctor Suárez-Paniagua, Isabel Segura-Bedmar, Paloma Martínez
Drug-drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning...
January 1, 2017: Database: the Journal of Biological Databases and Curation
https://www.readbyqxmd.com/read/28602906/counting-trees-in-random-forests-predicting-symptom-severity-in-psychiatric-intake-reports
#15
Elyne Scheurwegs, Madhumita Sushil, Stéphan Tulkens, Walter Daelemans, Kim Luyckx
The CEGS N-GRID 2016 Shared Task (Filannino, Stubbs, Uzuner (2017)) in Clinical Natural Language Processing introduces the assignment of a severity score to a psychiatric symptom, based on a psychiatric intake report. We present a method that employs the inherent interview-like structure of the report to extract relevant information from the report and generate a representation. The representation consists of a restricted set of psychiatric concepts (and the context they occur in), identified using medical concepts defined in UMLS that are directly related to the psychiatric diagnoses present in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) ontology...
June 7, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28602904/a-hybrid-approach-to-automatic-de-identification-of-psychiatric-notes
#16
Hee-Jin Lee, Yonghui Wu, Yaoyun Zhang, Jun Xu, Hua Xu, Kirk Roberts
De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research. This paper presents a natural language processing system for automatic de-identification of psychiatric notes, which was designed to participate in the 2016 CEGS N-GRID shared task Track 1. The system has a hybrid structure that combines machine leaning techniques and rule-based approaches. The rule-based components exploit the structure of the psychiatric notes as well as characteristic surface patterns of PHI mentions...
June 7, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28598934/feasibility-and-utility-of-lexical-analysis-for-occupational-health-text
#17
Philip Harber, Gondy Leroy
OBJECTIVE: Assess feasibility and potential utility of natural language processing (NLP) for storing and analyzing occupational health data. METHODS: Basic NLP lexical analysis methods were applied to 89,000 Mine Safety and Health Administration (MSHA) free text records. Steps included tokenization, term and co-occurrence counts, term annotation, and identifying exposure-health effect relationships. Presence of terms in the Unified Medical Language System (UMLS) was assessed...
June 2017: Journal of Occupational and Environmental Medicine
https://www.readbyqxmd.com/read/28587103/standfood-standardization-of-foods-using-a-semi-automatic-system-for-classifying-and-describing-foods-according-to-foodex2
#18
Tome Eftimov, Peter Korošec, Barbara Koroušić Seljak
The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed-a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc...
May 26, 2017: Nutrients
https://www.readbyqxmd.com/read/28579533/de-identification-of-clinical-notes-via-recurrent-neural-network-and-conditional-random-field
#19
Zengjian Liu, Buzhou Tang, Xiaolong Wang, Qingcai Chen
De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published. The 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-scale and RDOC Individualized Domains (N-GRID) clinical natural language processing (NLP) challenge contains a de-identification track in de-identifying electronic medical records (EMRs) (i.e., track 1). The challenge organizers provide 1000 annotated mental health records for this track, 600 out of which are used as a training set and 400 as a test set...
June 1, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28576748/automatic-recognition-of-symptom-severity-from-psychiatric-evaluation-records
#20
Travis R Goodwin, Ramon Maldonado, Sanda M Harabagiu
This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c) accounting for the role of negation...
May 30, 2017: Journal of Biomedical Informatics
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