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https://www.readbyqxmd.com/read/28925741/endophthalmitis-following-strabismus-surgery-iposc-global-study
#1
Ofira Zloto, Eedy Mezer, Luis Ospina, Branislav Stankovic, Tamara Wygnanski-Jaffe
PURPOSE: To examine the characteristics of patients with endophthalmitis after strabismus surgery (PSSE), the characteristics of the strabismus surgery, treatment, and prognosis as reported by pediatric ophthalmologists who are members of the American Association for Pediatric Ophthalmology and Strabismus (AAOPS) around the world. METHODS: An email communication was sent to all members of AAPOS. The email included a web link to a survey that included 34 questions that examined the characteristics of patients with endophthalmitis after strabismus surgery...
September 19, 2017: Current Eye Research
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/28898194/correlate-a-pacs-and-ehr-integrated-tool-leveraging-natural-language-processing-to-provide-automated-clinical-follow-up
#4
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/28893314/design-of-an-extensive-information-representation-scheme-for-clinical-narratives
#5
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/28881973/biosses-a-semantic-sentence-similarity-estimation-system-for-the-biomedical-domain
#6
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/28866574/visual-exploration-of-semantic-relationships-in-neural-word-embeddings
#7
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/28830409/word2vec-inversion-and-traditional-text-classifiers-for-phenotyping-lupus
#8
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
#9
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
#10
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
https://www.readbyqxmd.com/read/28815144/a-clinical-decision-support-system-for-monitoring-post-colonoscopy-patient-follow-up-and-scheduling
#11
Roxanne Wadia, Mark Shifman, Forrest L Levin, Luis Marenco, Cynthia A Brandt, Kei-Hoi Cheung, Tamar Taddei, Michael Krauthammer
This paper describes a natural language processing (NLP)-based clinical decision support (CDS) system that is geared towards colon cancer care coordinators as the end users. The system is implemented using a metadata- driven Structured Query Language (SQL) function (discriminant function). For our pilot study, we have developed a training corpus consisting of 2,085 pathology reports from the VA Connecticut Health Care System (VACHS). We categorized reports as "actionable"- requiring close follow up, or "non-actionable"- requiring standard or no follow up...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815141/identifying-metastases-related-information-from-pathology-reports-of-lung-cancer-patients
#12
Ergin Soysal, Jeremy L Warner, Joshua C Denny, Hua Xu
Metastatic patterns of spread at the time of cancer recurrence are one of the most important prognostic factors in estimation of clinical course and survival of the patient. This information is not easily accessible since it's rarely recorded in a structured format. This paper describes a system for categorization of pathology reports by specimen site and the detection of metastatic status within the report. A clinical NLP pipeline was developed using sentence boundary detection, tokenization, section identification, part-of-speech tagger, and chunker with some rule based methods to extract metastasis site and status in combination with five types of information related to tumor metastases: histological type, grade, specimen site, metastatic status indicators and the procedure...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815139/semanticfind-locating-what-you-want-in-a-patient-record-not-just-what-you-ask-for
#13
John M Prager, Jennifer J Liang, Murthy V Devarakonda
We present a new model of patient record search, called SemanticFind, which goes beyond traditional textual and medical synonym matches by locating patient data that a clinician would want to see rather than just what they ask for. The new model is implemented by making extensive use of the UMLS semantic network, distributional semantics, and NLP, to match query terms along several dimensions in a patient record with the returned matches organized accordingly. The new approach finds all clinically related concepts without the user having to ask for them...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815133/correlating-lab-test-results-in-clinical-notes-with-structured-lab-data-a-case-study-in-hba1c-and-glucose
#14
Sijia Liu, Liwei Wang, Donna Ihrke, Vipin Chaudhary, Cui Tao, Chunhua Weng, Hongfang Liu
It is widely acknowledged that information extraction of unstructured clinical notes using natural language processing (NLP) and text mining is essential for secondary use of clinical data for clinical research and practice. Lab test results are currently structured in most of the electronic health record (EHR) systems. However, for referral patients or lab tests that can be done in non-clinical setting, the results can be captured in unstructured clinical notes. In this study, we proposed a rule-based information extraction system to extract the lab test results with temporal information from clinical notes...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815130/ground-truth-creation-for-complex-clinical-nlp-tasks-an-iterative-vetting-approach-and-lessons-learned
#15
Jennifer J Liang, Ching-Huei Tsou, Murthy V Devarakonda
Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management. A critical need in developing such methods is the "ground truth" dataset needed for training and testing the algorithms. Beyond localizable, relatively simple tasks, ground truth creation is a significant challenge because medical experts, just as physicians in patient care, have to assimilate vast amounts of data in EHR systems...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815115/discovering-adverse-drug-events-combining-spontaneous-reports-with-electronic-medical-records-a-case-study-of-conventional-dmards-and-biologics-for-rheumatoid-arthritis
#16
Liwei Wang, Majid Rastegar-Mojarad, Sijia Liu, Huaji Zhang, Hongfang Liu
The use of multiple data sources has been preferred in the surveillance of adverse drug events due to shortcomings of using only a single source. In this study, we proposed a framework where the ADEs associated with interested drugs are systematically discovered from the FDA's Adverse Event Reporting System (AERS), and then validated through mining unstructured clinical notes from Electronic Medical Records (EMRs). This framework has two features. First, a higher priority was given to clinical practice during signal detection and validation...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815100/surveillance-of-peripheral-arterial-disease-cases-using-natural-language-processing-of-clinical-notes
#17
Naveed Afzal, Sunghwan Sohn, Christopher G Scott, Hongfang Liu, Iftikhar J Kullo, Adelaide M Arruda-Olson
Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide and yet remains underdiagnosed and undertreated. Early detection is important, because PAD is strongly associated with an increased risk of mortality and morbidity. In this study, we built a PAD surveillance system using natural language processing (NLP) for early detection of PAD from narrative clinical notes. Our NLP algorithm had excellent positive predictive value (0.93) and identified 41% of PAD cases before the initial ankle-brachial index (ABI) test date while in 12% of cases the NLP algorithm detected PAD on the same date as the ABI (the gold standard for comparison)...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28808792/using-natural-language-processing-of-free-text-radiology-reports-to-identify-type-1-modic-endplate-changes
#18
Hannu T Huhdanpaa, W Katherine Tan, Sean D Rundell, Pradeep Suri, Falgun H Chokshi, Bryan A Comstock, Patrick J Heagerty, Kathryn T James, Andrew L Avins, Srdjan S Nedeljkovic, David R Nerenz, David F Kallmes, Patrick H Luetmer, Karen J Sherman, Nancy L Organ, Brent Griffith, Curtis P Langlotz, David Carrell, Saeed Hassanpour, Jeffrey G Jarvik
Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine...
August 14, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28765137/childhood-respiratory-illness-presentation-and-service-utilisation-in-primary-care-a-six-year-cohort-study-in-wellington-new-zealand-using-natural-language-processing-nlp-software
#19
Anthony Dowell, Ben Darlow, Jayden Macrae, Maria Stubbe, Nikki Turner, Lynn McBain
OBJECTIVES: To identify childhood respiratory tract-related illness presentation rates and service utilisation in primary care by interrogating free text and coded data from electronic medical records. DESIGN: Retrospective cohort study. Data interrogation used a natural language processing software inference algorithm. SETTING: 36 primary care practices in New Zealand. Data analysed from January 2008 to December 2013. PARTICIPANTS: The records from 77 582 children enrolled were reviewed over a 6-year period to estimate the presentation of childhood respiratory illness and service utilisation...
August 1, 2017: BMJ Open
https://www.readbyqxmd.com/read/28761061/tepapa-a-novel-in-silico-feature-learning-pipeline-for-mining-prognostic-and-associative-factors-from-text-based-electronic-medical-records
#20
Frank Po-Yen Lin, Adrian Pokorny, Christina Teng, Richard J Epstein
Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed Text-based Exploratory Pattern Analyser for Prognosticator and Associator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest...
July 31, 2017: Scientific Reports
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