keyword
MENU ▼
Read by QxMD icon Read
search

"Natural Language Processing"

keyword
https://www.readbyqxmd.com/read/28919830/identification-of-patients-with-congenital-hemophilia-in-a-large-electronic-health-record-database
#1
Michael Wang, Anissa Cyhaniuk, David L Cooper, Neeraj N Iyer
BACKGROUND: Electronic health records (EHRs) are an important source of information with regard to diagnosis and treatment of rare health conditions, such as congenital hemophilia, a bleeding disorder characterized by deficiency of factor VIII (FVIII) or factor IX (FIX). OBJECTIVE: To identify patients with congenital hemophilia using EHRs. DESIGN: An EHR database study. SETTING: EHRs were accessed from Humedica between January 1, 2007, and July 31, 2013...
2017: Journal of Blood Medicine
https://www.readbyqxmd.com/read/28916254/development-of-a-natural-language-processing-engine-to-generate-bladder-cancer-pathology-data-for-health-services-research
#2
Florian R Schroeck, Olga V Patterson, Patrick R Alba, Erik A Pattison, John D Seigne, Scott L DuVall, Douglas J Robertson, Brenda Sirovich, Philip P Goodney
OBJECTIVE: To take a first step towards assembling population based cohorts of bladder cancer patients with longitudinal pathology data, we developed and validated a natural language processing (NLP) engine that abstracts pathology data from full text pathology reports. METHODS: Using 600 bladder pathology reports randomly selected from the Department of Veterans Affairs, we developed and validated an NLP engine to abstract data on histology, invasion (presence versus absence and depth), grade, presence of muscularis propria, and presence of carcinoma in situ...
September 12, 2017: Urology
https://www.readbyqxmd.com/read/28906424/accurate-identification-of-colonoscopy-quality-and-polyp-findings-using-natural-language-processing
#3
Jeffrey K Lee, Christopher D Jensen, Theodore R Levin, Ann G Zauber, Chyke A Doubeni, Wei K Zhao, Douglas A Corley
OBJECTIVES: The aim of this study was to test the ability of a commercially available natural language processing (NLP) tool to accurately extract examination quality-related and large polyp information from colonoscopy reports with varying report formats. BACKGROUND: Colonoscopy quality reporting often requires manual data abstraction. NLP is another option for extracting information; however, limited data exist on its ability to accurately extract examination quality and polyp findings from unstructured text in colonoscopy reports with different reporting formats...
September 12, 2017: Journal of Clinical Gastroenterology
https://www.readbyqxmd.com/read/28905434/decline-of-insulin-therapy-and-delays-in-insulin-initiation-in-people-with-uncontrolled-diabetes-mellitus
#4
N Hosomura, S Malmasi, D Timerman, V J Lei, H Zhang, L Chang, A Turchin
AIMS: To design and validate a natural language processing algorithm to identify insulin therapy decline from the text of physician notes, and to determine the prevalence of insulin therapy decline and its impact on insulin initiation. METHODS: We designed the algorithm using the publicly available natural language processing platform Canary. We evaluated the accuracy of the algorithm on 1501 randomly selected primary care physicians' notes from the electronic medical record system of a large academic medical centre...
September 14, 2017: Diabetic Medicine: a Journal of the British Diabetic Association
https://www.readbyqxmd.com/read/28898194/correlate-a-pacs-and-ehr-integrated-tool-leveraging-natural-language-processing-to-provide-automated-clinical-follow-up
#5
Mark D Kovacs, Joseph Mesterhazy, David Avrin, Thomas Urbania, John Mongan
A major challenge for radiologists is obtaining meaningful clinical follow-up information for even a small percentage of cases encountered and dictated. Traditional methods, such as keeping medical record number follow-up lists, discussing cases with rounding clinical teams, and discussing cases at tumor boards, are effective at keeping radiologists informed of clinical outcomes but are time intensive and provide follow-up for a small subset of cases. To this end, the authors developed a picture archiving and communication system-accessible electronic health record (EHR)-integrated program called Correlate, which allows the user to easily enter free-text search queries regarding desired clinical follow-up information, with minimal interruption to the workflow...
September 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/28895943/machine-learned-and-codified-synthesis-parameters-of-oxide-materials
#6
Edward Kim, Kevin Huang, Alex Tomala, Sara Matthews, Emma Strubell, Adam Saunders, Andrew McCallum, Elsa Olivetti
Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed...
September 12, 2017: Scientific Data
https://www.readbyqxmd.com/read/28893314/design-of-an-extensive-information-representation-scheme-for-clinical-narratives
#7
Louise Deléger, Leonardo Campillos, Anne-Laure Ligozat, Aurélie Névéol
BACKGROUND: Knowledge representation frameworks are essential to the understanding of complex biomedical processes, and to the analysis of biomedical texts that describe them. Combined with natural language processing (NLP), they have the potential to contribute to retrospective studies by unlocking important phenotyping information contained in the narrative content of electronic health records (EHRs). This work aims to develop an extensive information representation scheme for clinical information contained in EHR narratives, and to support secondary use of EHR narrative data to answer clinical questions...
September 11, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28888294/occupy-the-government-analyzing-presidential-and-congressional-discursive-response-to-movement-repression
#8
Joshua Gary Mausolf
I examine the role of Occupy Wall Street in shifting presidential and congressional discourse on economic fairness and inequality. Using data from 4646 presidential speeches and 1256 congressional records from 2009 to 2015, I test different mechanisms, including repression, media coverage, public opinion, and presidential agenda-setting by applying a novel combination of web scraping, natural language processing, and time series models. I suggest that movement success can be measured in its ability to shape discursive opportunity structures, and I argue that the role of the president should be at the forefront of social movements research...
September 2017: Social Science Research
https://www.readbyqxmd.com/read/28881973/biosses-a-semantic-sentence-similarity-estimation-system-for-the-biomedical-domain
#9
Gizem Sogancioglu, Hakime Öztürk, Arzucan Özgür
Motivation: The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English...
July 15, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28875048/text-mining-in-biomedical-domain-with-emphasis-on-document-clustering
#10
REVIEW
Vinaitheerthan Renganathan
OBJECTIVES: With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. METHODS: This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain...
July 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/28872869/demystifying-multi-task-deep-neural-networks-for-quantitative-structure-activity-relationships
#11
Yuting Xu, Junshui Ma, Andy Liaw, Robert P Sheridan, Vladimir Svetnik
Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision and natural language processing. In the past four years, DNNs also generated promising results for quantitative structure-activity relationship (QSAR) tasks. Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR datasets. It was also found that multi-task DNN models - those trained on and predicting multiple QSAR properties simultaneously - outperform DNNs trained separately on the individual datasets in many but not all tasks...
September 5, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28870101/effects-of-non-medical-switching-on-outcomes-among-patients-prescribed-tumor-necrosis-factor-inhibitors
#12
Allan Gibofsky, Martha Skup, Manish Mittal, Scott J Johnson, Matthew Davis, Jingdong Chao, David T Rubin
OBJECTIVE: To evaluate health care use and outcomes among patients who experienced a non-medical switch of their prescribed anti-tumor necrosis factor biological agent (anti-TNF) for cost containment reasons. METHODS: Retrospective evaluation of Humedica electronic health records of patients ≥18 years old with anti-TNF treatment for immune conditions. Using natural language processing, stable patients who experienced a non-medical switch (for cost or insurance reasons) of their anti-TNF between 2007 and 2013 were identified (NMS cohort, n = 158) and matched to patients who did not (control cohort, n = 4804)...
September 5, 2017: Current Medical Research and Opinion
https://www.readbyqxmd.com/read/28866574/visual-exploration-of-semantic-relationships-in-neural-word-embeddings
#13
Shusen Liu, Peer-Timo Bremer, Jayaraman J Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat, Valerio Pascucci
Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e...
August 29, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28866456/physician-characteristics-associated-with-higher-adenoma-detection-rate
#14
Ateev Mehrotra, Michele Morris, Rebecca A Gourevitch, David S Carrell, Daniel A Leffler, Sherri Rose, Julia B Greer, Seth D Crockett, Andrew Baer, Robert E Schoen
BACKGROUND & AIMS: Patients who receive a colonoscopy from a physician with a low adenoma detection rate are at higher risk of subsequent colorectal cancer. It is unclear what drives the variation across physicians in adenoma detection rate. We describe physician characteristics associated with higher ADR. METHODS: In this retrospective cohort study, a natural language processing system was used to analyze all outpatient colonoscopy examinations and their associated pathology reports from October 2013 to September 2015 for adults 40 and older across physicians from four diverse health systems...
August 30, 2017: Gastrointestinal Endoscopy
https://www.readbyqxmd.com/read/28861720/accurate-identification-of-fatty-liver-disease-in-data-warehouse-utilizing-natural-language-processing
#15
Joseph S Redman, Yamini Natarajan, Jason K Hou, Jingqi Wang, Muzammil Hanif, Hua Feng, Jennifer R Kramer, Roxanne Desiderio, Hua Xu, Hashem B El-Serag, Fasiha Kanwal
INTRODUCTION: Natural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records. METHODS: We utilized this technique to develop algorithms capable of "reading" full-text radiology reports to accurately identify the presence of fatty liver disease. Abdominal ultrasound, computerized tomography, and magnetic resonance imaging reports were retrieved from the Veterans Affairs Corporate Data Warehouse from a random national sample of 652 patients...
August 31, 2017: Digestive Diseases and Sciences
https://www.readbyqxmd.com/read/28841657/a-prediction-model-for-advanced-colorectal-neoplasia-in-an-asymptomatic-screening-population
#16
Sung Noh Hong, Hee Jung Son, Sun Kyu Choi, Dong Kyung Chang, Young-Ho Kim, Sin-Ho Jung, Poong-Lyul Rhee
BACKGROUND: An electronic medical record (EMR) database of a large unselected population who received screening colonoscopies may minimize sampling error and represent real-world estimates of risk for screening target lesions of advanced colorectal neoplasia (CRN). Our aim was to develop and validate a prediction model for assessing the probability of advanced CRN using a clinical data warehouse. METHODS: A total of 49,450 screenees underwent their first colonoscopy as part of a health check-up from 2002 to 2012 at Samsung Medical Center, and the dataset was constructed by means of natural language processing from the computerized EMR system...
2017: PloS One
https://www.readbyqxmd.com/read/28831738/epidemiology-from-tweets-estimating-misuse-of-prescription-opioids-in-the-usa-from-social-media
#17
Michael Chary, Nicholas Genes, Christophe Giraud-Carrier, Carl Hanson, Lewis S Nelson, Alex F Manini
BACKGROUND: The misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month. METHODS: We wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528)...
August 22, 2017: Journal of Medical Toxicology: Official Journal of the American College of Medical Toxicology
https://www.readbyqxmd.com/read/28830409/word2vec-inversion-and-traditional-text-classifiers-for-phenotyping-lupus
#18
Clayton A Turner, Alexander D Jacobs, Cassios K Marques, James C Oates, Diane L Kamen, Paul E Anderson, Jihad S Obeid
BACKGROUND: Identifying patients with certain clinical criteria based on manual chart review of doctors' notes is a daunting task given the massive amounts of text notes in the electronic health records (EHR). This task can be automated using text classifiers based on Natural Language Processing (NLP) techniques along with pattern recognition machine learning (ML) algorithms. The aim of this research is to evaluate the performance of traditional classifiers for identifying patients with Systemic Lupus Erythematosus (SLE) in comparison with a newer Bayesian word vector method...
August 22, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28816338/prediction-of-emergency-department-hospital-admission-based-on-natural-language-processing-and-neural-networks
#19
Xingyu Zhang, Joyce Kim, Rachel E Patzer, Stephen R Pitts, Aaron Patzer, Justin D Schrager
OBJECTIVE: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. METHODS: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs...
August 16, 2017: Methods of Information in Medicine
https://www.readbyqxmd.com/read/28815363/the-use-of-natural-language-processing-on-pediatric-diagnostic-radiology-reports-in-the-electronic-health-record-to-identify-deep-venous-thrombosis-in-children
#20
Jorge A Gálvez, Janine M Pappas, Luis Ahumada, John N Martin, Allan F Simpao, Mohamed A Rehman, Char Witmer
Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists' reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine's performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports...
August 16, 2017: Journal of Thrombosis and Thrombolysis
keyword
keyword
27002
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"