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"Natural Language Processing"

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https://www.readbyqxmd.com/read/28104580/leveraging-electronic-health-care-record-information-to-measure-pressure-ulcer-risk-in-veterans-with-spinal-cord-injury-a-longitudinal-study-protocol
#1
Stephen L Luther, Susan S Thomason, Sunil Sabharwal, Dezon K Finch, James McCart, Peter Toyinbo, Lina Bouayad, Michael E Matheny, Glenn T Gobbel, Gail Powell-Cope
BACKGROUND: Pressure ulcers (PrUs) are a frequent, serious, and costly complication for veterans with spinal cord injury (SCI). The health care team should periodically identify PrU risk, although there is no tool in the literature that has been found to be reliable, valid, and sensitive enough to assess risk in this vulnerable population. OBJECTIVE: The immediate goal is to develop a risk assessment model that validly estimates the probability of developing a PrU...
January 19, 2017: JMIR Research Protocols
https://www.readbyqxmd.com/read/28096249/natural-language-processing-to-extract-symptoms-of-severe-mental-illness-from-clinical-text-the-clinical-record-interactive-search-comprehensive-data-extraction-cris-code-project
#2
Richard G Jackson, Rashmi Patel, Nishamali Jayatilleke, Anna Kolliakou, Michael Ball, Genevieve Gorrell, Angus Roberts, Richard J Dobson, Robert Stewart
OBJECTIVES: We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research. DESIGN: Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries...
January 17, 2017: BMJ Open
https://www.readbyqxmd.com/read/28062392/variations-in-facebook-posting-patterns-across-validated-patient-health-conditions-a-prospective-cohort-study
#3
Robert J Smith, Patrick Crutchley, H Andrew Schwartz, Lyle Ungar, Frances Shofer, Kevin A Padrez, Raina M Merchant
BACKGROUND: Social media is emerging as an insightful platform for studying health. To develop targeted health interventions involving social media, we sought to identify the patient demographic and disease predictors of frequency of posting on Facebook. OBJECTIVE: The aims were to explore the language topics correlated with frequency of social media use across a cohort of social media users within a health care setting, evaluate the differences in the quantity of social media postings across individuals with different disease diagnoses, and determine if patients could accurately predict their own levels of social media engagement...
January 6, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28060227/preoperative-opioid-use-is-associated-with-early-revision-after-total-knee-arthroplasty-a-study-of-male-patients-treated-in-the-veterans-affairs-system
#4
Alon Ben-Ari, Howard Chansky, Irene Rozet
BACKGROUND: Opioid use is endemic in the U.S. and is associated with morbidity and mortality. The impact of long-term opioid use on joint-replacement outcomes remains unknown. We tested the hypothesis that use of opioids is associated with adverse outcomes after total knee arthroplasty (TKA). METHODS: We performed a retrospective analysis of patients who had had TKA within the U.S. Veterans Affairs (VA) system over a 6-year period and had been followed for 1 year postoperatively...
January 4, 2017: Journal of Bone and Joint Surgery. American Volume
https://www.readbyqxmd.com/read/28055847/show-and-tell-lessons-learned-from-the-2015-mscoco-image-captioning-challenge
#5
Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions...
July 7, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28052483/natural-language-processing-to-ascertain-two-key-variables-from-operative-reports-in-ophthalmology
#6
Liyan Liu, Neal H Shorstein, Laura B Amsden, Lisa J Herrinton
PURPOSE: Antibiotic prophylaxis is critical to ophthalmology and other surgical specialties. We performed natural language processing (NLP) of 743 838 operative notes recorded for 315 246 surgeries to ascertain two variables needed to study the comparative effectiveness of antibiotic prophylaxis in cataract surgery. The first key variable was an exposure variable, intracameral antibiotic injection. The second was an intraoperative complication, posterior capsular rupture (PCR), which functioned as a potential confounder...
January 3, 2017: Pharmacoepidemiology and Drug Safety
https://www.readbyqxmd.com/read/28050745/an-evolving-ecosystem-for-natural-language-processing-in-department-of-veterans-affairs
#7
Jennifer H Garvin, Megha Kalsy, Cynthia Brandt, Stephen L Luther, Guy Divita, Gregory Coronado, Doug Redd, Carrie Christensen, Brent Hill, Natalie Kelly, Qing Zeng Treitler
In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requirements and formative evaluation data from the Veterans Affairs (VA) Clinical NLP Ecosystem stakeholders using semi-structured interviews and meeting discussions. We developed a coding rubric to code interviews. We assessed inter-coder reliability using percent agreement and the kappa statistic...
February 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28050714/characterization-of-change-and-significance-for-clinical-findings-in-radiology-reports-through-natural-language-processing
#8
Saeed Hassanpour, Graham Bay, Curtis P Langlotz
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize the underlying semantics of radiology report narratives for this task...
January 3, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28034982/enhancing-risk-assessment-in-patients-receiving-chronic-opioid-analgesic-therapy-using-natural-language-processing
#9
Irina V Haller, Colleen M Renier, Mitch Juusola, Paul Hitz, William Steffen, Michael J Asmus, Terri Craig, Jack Mardekian, Elizabeth T Masters, Thomas E Elliott
OBJECTIVES:  Clinical guidelines for the use of opioids in chronic noncancer pain recommend assessing risk for aberrant drug-related behaviors prior to initiating opioid therapy. Despite recent dramatic increases in prescription opioid misuse and abuse, use of screening tools by clinicians continues to be underutilized. This research evaluated natural language processing (NLP) together with other data extraction techniques for risk assessment of patients considered for opioid therapy as a means of predicting opioid abuse...
December 29, 2016: Pain Medicine: the Official Journal of the American Academy of Pain Medicine
https://www.readbyqxmd.com/read/28032554/detection-of-clinically-important-colorectal-surgical-site-infection-using-bayesian-network
#10
Sunghwan Sohn, David W Larson, Elizabeth B Habermann, James M Naessens, Jasim Y Alabbad, Hongfang Liu
BACKGROUND: Despite extensive efforts to monitor and prevent surgical site infections (SSIs), real-time surveillance of clinical practice has been sparse and expensive or nonexistent. However, natural language processing (NLP) and machine learning (i.e., Bayesian network analysis) may provide the methodology necessary to approach this issue in a new way. We investigated the ability to identify SSIs after colorectal surgery (CRS) through an automated detection system using a Bayesian network...
October 5, 2016: Journal of Surgical Research
https://www.readbyqxmd.com/read/28006016/measuring-emotion-in-parliamentary-debates-with-automated-textual-analysis
#11
Ludovic Rheault, Kaspar Beelen, Christopher Cochrane, Graeme Hirst
An impressive breadth of interdisciplinary research suggests that emotions have an influence on human behavior. Nonetheless, we still know very little about the emotional states of those actors whose daily decisions have a lasting impact on our societies: politicians in parliament. We address this question by making use of methods of natural language processing and a digitized corpus of text data spanning a century of parliamentary debates in the United Kingdom. We use this approach to examine changes in aggregate levels of emotional polarity in the British parliament, and to test a hypothesis about the emotional response of politicians to economic recessions...
2016: PloS One
https://www.readbyqxmd.com/read/27997952/algorithmic-classification-of-five-characteristic-types-of-paraphasias
#12
Gerasimos Fergadiotis, Kyle Gorman, Steven Bedrick
Purpose: This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). Method: We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions...
December 1, 2016: American Journal of Speech-language Pathology
https://www.readbyqxmd.com/read/27994938/the-utility-of-including-pathology-reports-in-improving-the-computational-identification-of-patients
#13
Wei Chen, Yungui Huang, Brendan Boyle, Simon Lin
BACKGROUND: Celiac disease (CD) is a common autoimmune disorder. Efficient identification of patients may improve chronic management of the disease. Prior studies have shown searching International Classification of Diseases-9 (ICD-9) codes alone is inaccurate for identifying patients with CD. In this study, we developed automated classification algorithms leveraging pathology reports and other clinical data in Electronic Health Records (EHRs) to refine the subset population preselected using ICD-9 code (579...
2016: Journal of Pathology Informatics
https://www.readbyqxmd.com/read/27989816/extractive-text-summarization-system-to-aid-data-extraction-from-full-text-in-systematic-review-development
#14
Duy Duc An Bui, Guilherme Del Fiol, John F Hurdle, Siddhartha Jonnalagadda
OBJECTIVES: Extracting data from publication reports is a standard process in systematic review (SR) development. However, the data extraction process still relies too much on manual effort which is slow, costly, and subject to human error. In this study, we developed a text summarization system aimed at enhancing productivity and reducing errors in the traditional data extraction process. METHODS: We developed a computer system that used machine learning and natural language processing approaches to automatically generate summaries of full-text scientific publications...
October 27, 2016: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/27981203/a-corpus-for-mining-drug-related-knowledge-from-twitter-chatter-language-models-and-their-utilities
#15
Abeed Sarker, Graciela Gonzalez
In this data article, we present to the data science, natural language processing and public heath communities an unlabeled corpus and a set of language models. We collected the data from Twitter using drug names as keywords, including their common misspelled forms. Using this data, which is rich in drug-related chatter, we developed language models to aid the development of data mining tools and methods in this domain. We generated several models that capture (i) distributed word representations and (ii) probabilities of n-gram sequences...
February 2017: Data in Brief
https://www.readbyqxmd.com/read/27977903/elementary-my-dear-watson-the-era-of-natural-language-processing-in-transplantation
#16
EDITORIAL
Bing Ho, Anton Skaro, Samantha Montag, Lihui Zhao
The use of modern data acquisition and analytics commonly in use in the technology sector by Google, IBM, and others have long been the envy of the health services researcher. In this issue, Srinivas et al. have demonstrated feasibility in the use of data mining and natural language processing (NLP) in data abstraction.(1) In their study they developed predictive models for graft loss and patient survival in kidney transplantation using single-center retrospective data. This article is protected by copyright...
December 15, 2016: American Journal of Transplantation
https://www.readbyqxmd.com/read/27970563/automatic-extraction-and-classification-of-patients-smoking-status-from-free-text-using-natural-language-processing
#17
A Caccamisi, L Jörgensen, H Dalianis, M Rosenlund
No abstract text is available yet for this article.
November 2016: Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research
https://www.readbyqxmd.com/read/27970465/accuracy-of-natural-language-processing-based-classifiers-for-automated-identification-of-abstracts-of-studies-on-humanistic-and-economic-burden-of-disease
#18
J Krohn, A Martin, C Martin
No abstract text is available yet for this article.
November 2016: Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research
https://www.readbyqxmd.com/read/27959834/simplifying-ehr-overview-of-critically-ill-patients-through-vital-signs-monitoring
#19
Adnan Vilic, Karsten Hoppe, John Petersen, Troels Kjaer, Helge Sorensen
This paper presents a novel data-driven approach to graphical presentation of text-based electronic health records (EHR) while maintaining all textual information. We have developed the Patient Condition Timeline (PCT) tool, which creates a timeline representation of a patients' physiological condition during admission. PCT is based on electronical monitoring of vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on existing EHR to extract all entries...
December 9, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27955641/microbial-phenomics-information-extractor-micropie-a-natural-language-processing-tool-for-the-automated-acquisition-of-prokaryotic-phenotypic-characters-from-text-sources
#20
Jin Mao, Lisa R Moore, Carrine E Blank, Elvis Hsin-Hui Wu, Marcia Ackerman, Sonali Ranade, Hong Cui
BACKGROUND: The large-scale analysis of phenomic data (i.e., full phenotypic traits of an organism, such as shape, metabolic substrates, and growth conditions) in microbial bioinformatics has been hampered by the lack of tools to rapidly and accurately extract phenotypic data from existing legacy text in the field of microbiology. To quickly obtain knowledge on the distribution and evolution of microbial traits, an information extraction system needed to be developed to extract phenotypic characters from large numbers of taxonomic descriptions so they can be used as input to existing phylogenetic analysis software packages...
December 13, 2016: BMC Bioinformatics
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