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"Natural Language Processing"

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https://www.readbyqxmd.com/read/28742789/evaluation-of-appropriate-venous-thromboembolism-prophylaxis-in-orthopaedic-trauma-patients-with-symptom-driven-vascular-and-radiographic-studies
#1
Christopher M Domes, Anneliese M Schleyer, James M McQueen, Ronald F Pergamit, Daphne M Beingessner
OBJECTIVE: To evaluate venous thromboembolism (VTE) prophylaxis adherence and effectiveness in orthopaedic trauma patients who had vascular or radiographic studies showing deep vein thromboses (DVTs) or pulmonary emboli (PEs). DESIGN: Retrospective review SETTING:: This study was conducted at a level I trauma center that independently services a 5 state region. The medical records of patients treated surgically between July 2010 and March 2013 were interrogated using a technical tool that electronically captures thrombotic event data from vascular and radiologic imaging studies via natural language processing...
July 24, 2017: Journal of Orthopaedic Trauma
https://www.readbyqxmd.com/read/28738423/continued-statin-prescriptions-after-adverse-reactions-and-patient-outcomes-a-cohort-study
#2
Huabing Zhang, Jorge Plutzky, Maria Shubina, Alexander Turchin
Background: Many patients discontinue statin treatment, often after having a possible adverse reaction. The risks and benefits of continued statin therapy after an adverse reaction are not known. Objective: To examine the relationship between continuation of statin therapy (any prescription within 12 months after an adverse reaction) and clinical outcomes. Design: Retrospective cohort study. Setting: Primary care practices affiliated with 2 academic medical centers...
July 25, 2017: Annals of Internal Medicine
https://www.readbyqxmd.com/read/28736776/emojinet-building-a-machine-readable-sense-inventory-for-emoji
#3
Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or 'sense' of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning...
November 2016: Proc Int Workshop Soc Inform
https://www.readbyqxmd.com/read/28734863/significant-linkage-evidence-for-interstitial-cystitis-painful-bladder-syndrome-on-chromosome-3
#4
Kristina Allen-Brady, Kerry Rowe, Melissa Cessna, Sara Lenherr, Peggy Norton
PURPOSE: Interstitial cystitis/painful bladder syndrome (IC/PBS) is a chronic pelvic pain condition with unknown etiology. We hypothesized that related IC/PBS cases were more likely to have a genetic etiology. The purpose of this study was to perform a genetic linkage analysis. MATERIALS AND METHODS: IC/PBS cases were identified using diagnostic codes linked to a population-based genealogy resource (Utah Population Database) and electronic medical records. For this analysis, 13 high-risk pedigrees (defined as having a statistical excess number of IC/PBS cases among descendants compared to matched hospital population rates) were used...
July 19, 2017: Journal of Urology
https://www.readbyqxmd.com/read/28729030/natural-language-processing-systems-for-capturing-and-standardizing-unstructured-clinical-information-a-systematic-review
#5
REVIEW
Kory Kreimeyer, Matthew Foster, Abhishek Pandey, Nina Arya, Gwendolyn Halford, Sandra F Jones, Richard Forshee, Mark Walderhaug, Taxiarchis Botsis
We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers...
July 17, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28716789/identification-of-the-delivery-of-cognitive-behavioural-therapy-for-psychosis-cbtp-using-a-cross-sectional-sample-from-electronic-health-records-and-open-text-information-in-a-large-uk-based-mental-health-case-register
#6
Craig Colling, Lauren Evans, Matthew Broadbent, David Chandran, Thomas J Craig, Anna Kolliakou, Robert Stewart, Philippa A Garety
OBJECTIVE: Our primary objective was to identify cognitive behavioural therapy (CBT) delivery for people with psychosis (CBTp) using an automated method in a large electronic health record database. We also examined what proportion of service users with a diagnosis of psychosis were recorded as having received CBTp within their episode of care during defined time periods provided by early intervention or promoting recovery community services for people with psychosis, compared with published audits and whether demographic characteristics differentially predicted the receipt of CBTp...
July 17, 2017: BMJ Open
https://www.readbyqxmd.com/read/28714447/feasibility-of-automating-patient-acuity-measurement-using-a-machine-learning-algorithm
#7
Caitlin W Brennan, Frank Meng, Mark M Meterko, Leonard W D'Avolio
BACKGROUND AND PURPOSE: One method of determining nurse staffing is to match patient demand for nursing care (patient acuity) with available nursing staff. This pilot study explored the feasibility of automating acuity measurement using a machine learning algorithm. METHODS: Natural language processing combined with a machine learning algorithm was used to predict acuity levels based on electronic health record data. RESULTS: The algorithm was able to predict acuity relatively well...
December 1, 2016: Journal of Nursing Measurement
https://www.readbyqxmd.com/read/28707176/estimating-the-average-need-of-semantic-knowledge-from-distributional-semantic-models
#8
Geoff Hollis
Continuous bag of words (CBOW) and skip-gram are two recently developed models of lexical semantics (Mikolov, Chen, Corrado, & Dean, Advances in Neural Information Processing Systems, 26, 3111-3119, 2013). Each has been demonstrated to perform markedly better at capturing human judgments about semantic relatedness than competing models (e.g., latent semantic analysis; Landauer & Dumais, Psychological Review, 104(2), 1997 211; hyperspace analogue to language; Lund & Burgess, Behavior Research Methods, Instruments, & Computers, 28(2), 203-208, 1996)...
July 13, 2017: Memory & Cognition
https://www.readbyqxmd.com/read/28699566/entity-recognition-from-clinical-texts-via-recurrent-neural-network
#9
Zengjian Liu, Ming Yang, Xiaolong Wang, Qingcai Chen, Buzhou Tang, Zhe Wang, Hua Xu
BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years...
July 5, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28699553/introduction-the-international-conference-on-intelligent-biology-and-medicine-icibm-2016-special-focus-on-medical-informatics-and-big-data
#10
Cui Tao, Yang Gong, Hua Xu, Zhongming Zhao
In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) held on December 8-10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. At ICIBM 2016, a special theme, "Medical Informatics and Big Data," was dedicated to the recent advances of data science in the medical domain. After peer review, ten articles were selected for this special issue, covering topics such as Knowledge and Data Personalization, Social Media Applications to Healthcare, Clinical Natural Language Processing, Patient Safety Analyses, and Data Mining Using Electronic Health Records...
July 5, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28699546/an-active-learning-enabled-annotation-system-for-clinical-named-entity-recognition
#11
Yukun Chen, Thomas A Lask, Qiaozhu Mei, Qingxia Chen, Sungrim Moon, Jingqi Wang, Ky Nguyen, Tolulola Dawodu, Trevor Cohen, Joshua C Denny, Hua Xu
BACKGROUND: Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain. METHODS: In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models...
July 5, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28683215/monitoring-lung-cancer-screening-utilization-and-outcomes-in-four-cancer-research-network-sites
#12
Michael K Gould, Lori C Sakoda, Debra P Ritzwoller, Michael J Simoff, Christine M Neslund-Dudas, Lawrence H Kushi, Lisa Carter-Harris, Heather Spencer Feigelson, George Minowada, V Paul Doria-Rose
RATIONALE: Lung cancer screening registries can monitor screening outcomes and improve quality of care. OBJECTIVES: To describe nascent lung cancer screening programs and share efficient data collection approaches for mandatory registry reporting in four integrated health care systems of the NCI-funded Cancer Research Network. METHODS: We documented the distinctive characteristics of lung cancer screening programs, provided examples of strategies to facilitate data collection, and described early challenges and possible solutions...
July 6, 2017: Annals of the American Thoracic Society
https://www.readbyqxmd.com/read/28679904/using-structured-and-unstructured-data-to-refine-estimates-of-military-sexual-trauma-status-among-us-military-veterans
#13
Adi V Gundlapalli, Emily Brignone, Guy Divita, Audrey L Jones, Andrew Redd, Ying Suo, Warren B P Pettey, April Mohanty, Lori Gawron, Rebecca Blais, Matthew H Samore, Jamison D Fargo
Sexual trauma survivors are reluctant to disclose such a history due to stigma. This is likely the case when estimating the prevalence of sexual trauma experienced in the military. The Veterans Health Administration has a program by which all former US military service members (Veterans) are screened for military sexual trauma (MST) using a questionnaire. Administrative data on MST screens and a change of status from an initial negative answer to positive and natural language processing (NLP) on electronic medical notes to extract concepts related to MST were used to refine initial estimates of MST among a random sample of 20,000 Veterans...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28671914/assessment-of-automating-safety-surveillance-from-electronic-health-records-analysis-for-the-quality-and-safety-review-system
#14
Allan Fong, Katharine Adams, Anita Samarth, Laura McQueen, Manan Trivedi, Tahleah Chappel, Erin Grace, Susan Terrillion, Raj M Ratwani
BACKGROUND AND OBJECTIVES: In an effort to improve and standardize the collection of adverse event data, the Agency for Healthcare Research and Quality is developing and testing a patient safety surveillance system called the Quality and Safety Review System (QSRS). Its current abstraction from medical records is through manual human coders, taking an average of 75 minutes to complete the review and abstraction tasks for one patient record. With many healthcare systems across the country adopting electronic health record (EHR) technology, there is tremendous potential for more efficient abstraction by automatically populating QSRS...
June 30, 2017: Journal of Patient Safety
https://www.readbyqxmd.com/read/28664200/an-ontology-enabled-natural-language-processing-pipeline-for-provenance-metadata-extraction-from-biomedical-text-short-paper
#15
Joshua Valdez, Michael Rueschman, Matthew Kim, Susan Redline, Satya S Sahoo
Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility...
October 2016: On Move Meaningful Internet Syst
https://www.readbyqxmd.com/read/28648889/mapping-between-fmri-responses-to-movies-and-their-natural-language-annotations
#16
REVIEW
Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang, Christopher Baldassano, Janice Chen, Esther Yong, Christopher Honey, Uri Hasson, Peter Ramadge, Kenneth A Norman, Sanjeev Arora
Several research groups have shown how to map fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock (Chen et al., 2017), and learn bidirectional mappings between fMRI responses and natural language representations. By leveraging data from multiple subjects watching the same movie, we were able to perform scene classification with 72% accuracy (random guessing would give 4%) and scene ranking with average rank in the top 4% (random guessing would give 50%)...
June 22, 2017: NeuroImage
https://www.readbyqxmd.com/read/28643174/natural-language-processing-for-ehr-based-pharmacovigilance-a-structured-review
#17
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
#18
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
#19
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
#20
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
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