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"Natural Language Processing"

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https://www.readbyqxmd.com/read/28428140/automated-annotation-and-classification-of-bi-rads-assessment-from-radiology-reports
#1
Sergio M Castro, Eugene Tseytlin, Olga Medvedeva, Kevin Mitchell, Shyam Visweswaran, Tanja Bekhuis, Rebecca S Jacobson
The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach...
April 17, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28423796/personalized-guideline-based-treatment-recommendations-using-natural-language-processing-techniques
#2
Matthias Becker, Britta Böckmann
Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization. Today, electronic representation of clinical guidelines exists as unstructured text, but is not well-integrated with patient-specific information from electronic health records. Consequently, generic content of the clinical guidelines is accessible, but it is not possible to visualize the position of the patient on the clinical pathway, decision support cannot be provided by personalized guidelines for the next treatment step...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423792/acronym-disambiguation-in-spanish-electronic-health-narratives-using-machine-learning-techniques
#3
Ignacio Rubio-López, Roberto Costumero, Héctor Ambit, Consuelo Gonzalo-Martín, Ernestina Menasalvas, Alejandro Rodríguez González
Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches. Current approaches to process the unstructured texts in EHRs are based in applying text mining or natural language processing (NLP) techniques over the data. In particular Named Entity Recognition (NER) is of paramount importance to retrieve specific biomedical concepts from the text providing the semantic type of the concept retrieved...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423791/medical-text-classification-using-convolutional-neural-networks
#4
Mark Hughes, Irene Li, Spyros Kotoulas, Toyotaro Suzumura
We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423782/developing-a-manually-annotated-corpus-of-clinical-letters-for-breast-cancer-patients-on-routine-follow-up
#5
Graham Pitson, Patricia Banks, Lawrence Cavedon, Karin Verspoor
This paper introduces the annotation schema and annotation process for a corpus of clinical letters describing the disease course and treatment of oestrogen receptor positive breast cancer patients, after completion of primary surgery and radiotherapy treatment. Concepts related to therapy, clinical signs, and recurrence, as well as relationships linking these, are identified and annotated in 200 letters. This corpus will provide the basis for development of natural language processing tools for automatic extraction of key clinical factors from such letters...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423780/development-and-evaluation-of-a-case-based-retrieval-service
#6
Emilie Pasche, Marcello Chinali, Julien Gobeill, Patrick Ruch
Identifying similar patients might greatly facilitate the treatment of a given patient, enabling to observe the response and outcome to a particular treatment. Case-based retrieval services dealing with natural language processing are of major importance to deal with the significant amount of unstructured clinical data. In this paper, we present the development and evaluation of a case-based retrieval (CBR) service tested on a collection of Italian pediatric cardiology cases. Cases are indexed and a search engine is proposed...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28419261/challenges-in-adapting-existing-clinical-natural-language-processing-systems-to-multiple-diverse-health-care-settings
#7
David S Carrell, Robert E Schoen, Daniel A Leffler, Michele Morris, Sherri Rose, Andrew Baer, Seth D Crockett, Rebecca A Gourevitch, Katie M Dean, Ateev Mehrotra
Objective: Widespread application of clinical natural language processing (NLP) systems requires taking existing NLP systems and adapting them to diverse and heterogeneous settings. We describe the challenges faced and lessons learned in adapting an existing NLP system for measuring colonoscopy quality. Materials and Methods: Colonoscopy and pathology reports from 4 settings during 2013-2015, varying by geographic location, practice type, compensation structure, and electronic health record...
April 17, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/28412959/opportunities-for-developing-therapies-for-rare-genetic-diseases-focus-on-gain-of-function-and-allostery
#8
Binbin Chen, Russ B Altman
BACKGROUND: Advances in next generation sequencing technologies have revolutionized our ability to discover the causes of rare genetic diseases. However, developing treatments for these diseases remains challenging. In fact, when we systematically analyze the US FDA orphan drug list, we find that only 8% of rare diseases have an FDA-designated drug. Our approach leverages three primary insights: first, diseases with gain-of-function mutations and late onset are more likely to have drug options; second, drugs are more often inhibitors than activators; and third, some disease-causing proteins can be rescued by allosteric activators in diseases due to loss-of-function mutations...
April 17, 2017: Orphanet Journal of Rare Diseases
https://www.readbyqxmd.com/read/28410513/representation-learning-via-dual-autoencoder-for-recommendation
#9
Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, Qing He
Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results...
March 27, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28410343/patient-understanding-of-the-risks-and-benefits-of-biologic-therapies-in-inflammatory-bowel-disease-insights-from-a-large-scale-analysis-of-social-media-platforms
#10
Bibiana Martinez, Francis Dailey, Christopher V Almario, Michelle S Keller, Mansee Desai, Taylor Dupuy, Sasan Mosadeghi, Cynthia Whitman, Karen Lasch, Lyann Ursos, Brennan M R Spiegel
BACKGROUND: Few studies have examined inflammatory bowel disease (IBD) patients' knowledge and understanding of biologic therapies outside traditional surveys. Here, we used social media data to examine IBD patients' understanding of the risks and benefits associated with biologic therapies and how this affects decision-making. METHODS: We collected posts from Twitter and e-forum discussions from >3000 social media sites posted between June 27, 2012 and June 27, 2015...
April 13, 2017: Inflammatory Bowel Diseases
https://www.readbyqxmd.com/read/28410049/a-customized-attention-based-long-short-term-memory-network-for-distant-supervised-relation-extraction
#11
Dengchao He, Hongjun Zhang, Wenning Hao, Rui Zhang, Kai Cheng
Distant supervision, a widely applied approach in the field of relation extraction can automatically generate large amounts of labeled training corpus with minimal manual effort. However, the labeled training corpus may have many false-positive data, which would hurt the performance of relation extraction. Moreover, in traditional feature-based distant supervised approaches, extraction models adopt human design features with natural language processing. It may also cause poor performance. To address these two shortcomings, we propose a customized attention-based long short-term memory network...
April 14, 2017: Neural Computation
https://www.readbyqxmd.com/read/28404537/building-a-comprehensive-syntactic-and-semantic-corpus-of-chinese-clinical-texts
#12
Bin He, Bin Dong, Yi Guan, Jinfeng Yang, Zhipeng Jiang, Qiubin Yu, Jianyi Cheng, Chunyan Qu
OBJECTIVE: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain. MATERIALS AND METHODS: An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation quality assurance measures, a comprehensive corpus was built, containing annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions, and relations...
April 9, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28403158/what-can-we-learn-from-corporate-sustainability-reporting-deriving-propositions-for-research-and-practice-from-over-9-500-corporate-sustainability-reports-published-between-1999-and-2015-using-topic-modelling-technique
#13
Nadine Székely, Jan Vom Brocke
Organizations are increasingly using sustainability reports to inform their stakeholders and the public about their sustainability practices. We apply topic modelling to 9,514 sustainability reports published between 1999 and 2015 in order to identify common topics and, thus, the most common practices described in these reports. In particular, we identify forty-two topics that reflect sustainability and focus on the coverage and trends of economic, environmental, and social sustainability topics. Among the first to analyse such a large amount of data on organizations' sustainability reporting, the paper serves as an example of how to apply natural language processing as a strategy of inquiry in sustainability research...
2017: PloS One
https://www.readbyqxmd.com/read/28396836/down-regulation-of-mir-146a-5p-and-its-potential-targets-in-hepatocellular-carcinoma-validated-by-a-tcga-and-geo-based-study
#14
Xin Zhang, Zhi-Hua Ye, Hai-Wei Liang, Fang-Hui Ren, Ping Li, Yi-Wu Dang, Gang Chen
Our previous research has demonstrated that miR-146a-5p is down-regulated in hepatocellular carcinoma (HCC) and might play a tumor-suppressive role. In this study, we sought to validate the decreased expression with a larger cohort and to explore potential molecular mechanisms. GEO and TCGA databases were used to gather miR-146a-5p expression data in HCC, which included 762 HCC and 454 noncancerous liver tissues. A meta-analysis of the GEO-based microarrays, TCGA-based RNA-seq data, and additional qRT-PCR data validated the down-regulation of miR-146a-5p in HCC and no publication bias was observed...
April 2017: FEBS Open Bio
https://www.readbyqxmd.com/read/28396404/eight-minute-self-regulation-intervention-raises-educational-attainment-at-scale-in-individualist-but-not-collectivist-cultures
#15
René F Kizilcec, Geoffrey L Cohen
Academic credentials open up a wealth of opportunities. However, many people drop out of educational programs, such as community college and online courses. Prior research found that a brief self-regulation strategy can improve self-discipline and academic outcomes. Could this strategy support learners at large scale? Mental contrasting with implementation intentions (MCII) involves writing about positive outcomes associated with a goal, the obstacles to achieving it, and concrete if-then plans to overcome them...
April 10, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28380048/a-study-of-the-transferability-of-influenza-case-detection-systems-between-two-large-healthcare-systems
#16
Ye Ye, Michael M Wagner, Gregory F Cooper, Jeffrey P Ferraro, Howard Su, Per H Gesteland, Peter J Haug, Nicholas E Millett, John M Aronis, Andrew J Nowalk, Victor M Ruiz, Arturo López Pineda, Lingyun Shi, Rudy Van Bree, Thomas Ginter, Fuchiang Tsui
OBJECTIVES: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. METHODS: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH)...
2017: PloS One
https://www.readbyqxmd.com/read/28375665/application-of-a-natural-language-processing-algorithm-to-asthma-ascertainment-an-automated-chart-review
#17
Chung-Il Wi, Sunghwan Sohn, Mary C Rolfes, Alicia Seabright, Euijung Ryu, Gretchen Voge, Kay A Bachman, Miguel A Park, Hirohito Kita, Ivana T Croghan, Hongfang Liu, Young J Juhn
RATIONALE: Difficulty of asthma ascertainment and its associated methodological heterogeneity have created significant barriers to asthma care and research. OBJECTIVES: We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review utilizing electronic medical records (EMRs). METHODS: The study was designed as a retrospective birth cohort study utilizing a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester...
April 4, 2017: American Journal of Respiratory and Critical Care Medicine
https://www.readbyqxmd.com/read/28368170/computerized-scoring-algorithms-for-the-autobiographical-memory-test
#18
Keisuke Takano, Charlotte Gutenbrunner, Kris Martens, Karen Salmon, Filip Raes
Reduced specificity of autobiographical memories is a hallmark of depressive cognition. Autobiographical memory (AM) specificity is typically measured by the Autobiographical Memory Test (AMT), in which respondents are asked to describe personal memories in response to emotional cue words. Due to this free descriptive responding format, the AMT relies on experts' hand scoring for subsequent statistical analyses. This manual coding potentially impedes research activities in big data analytics such as large epidemiological studies...
April 3, 2017: Psychological Assessment
https://www.readbyqxmd.com/read/28347453/creation-of-a-simple-natural-language-processing-tool-to-support-an-imaging-utilization-quality-dashboard
#19
Jordan Swartz, Christian Koziatek, Jason Theobald, Silas Smith, Eduardo Iturrate
BACKGROUND: Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of imaging utilization at the provider level, it is important to know that provider's diagnostic yield (percentage of tests positive for the diagnostic entity of interest). However, determining diagnostic yield typically requires either time-consuming, manual review of radiology reports or the use of complex and/or proprietary natural language processing software...
May 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/28339747/deep-learning-for-pharmacovigilance-recurrent-neural-network-architectures-for-labeling-adverse-drug-reactions-in-twitter-posts
#20
Anne Cocos, Alexander G Fiks, Aaron J Masino
Objective: Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media...
February 22, 2017: Journal of the American Medical Informatics Association: JAMIA
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