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"Natural Language Processing"

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https://www.readbyqxmd.com/read/28339747/deep-learning-for-pharmacovigilance-recurrent-neural-network-architectures-for-labeling-adverse-drug-reactions-in-twitter-posts
#1
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
https://www.readbyqxmd.com/read/28333934/visualizing-the-structure-of-rna-seq-expression-data-using-grade-of-membership-models
#2
Kushal K Dey, Chiaowen Joyce Hsiao, Matthew Stephens
Grade of membership models, also known as "admixture models", "topic models" or "Latent Dirichlet Allocation", are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to model admixed individuals who have ancestry from multiple "populations", and in natural language processing to model documents having words from multiple "topics". Here we illustrate the potential for these models to cluster samples of RNA-seq gene expression data, measured on either bulk samples or single cells...
March 2017: PLoS Genetics
https://www.readbyqxmd.com/read/28328520/a-natural-language-processing-framework-for-assessing-hospital-readmissions-for-patients-with-copd
#3
Ankur Agarwal, Christopher Baechle, Ravi Behara, Xingquan Zhu
With the passage of recent federal legislation many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and Chronic Obstructive Pulmonary Disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have been efforts to statistically predict those most in danger of readmission, few have focused primarily on unstructured clinical notes...
March 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28327593/characterisation-of-mental-health-conditions-in-social-media-using-informed-deep-learning
#4
George Gkotsis, Anika Oellrich, Sumithra Velupillai, Maria Liakata, Tim J P Hubbard, Richard J B Dobson, Rina Dutta
The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients' own experiences, and only record cross-sectional, professional impressions at the point of care...
March 22, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28316892/from-big-data-to-diagnosis-and-prognosis-gene-expression-signatures-in-liver-hepatocellular-carcinoma
#5
Hong Yang, Xin Zhang, Xiao-Yong Cai, Dong-Yue Wen, Zhi-Hua Ye, Liang Liang, Lu Zhang, Han-Lin Wang, Gang Chen, Zhen-Bo Feng
BACKGROUND: Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in. METHODS: Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes...
2017: PeerJ
https://www.readbyqxmd.com/read/28299240/validation-of-the-total-visual-acuity-extraction-algorithm-tova-for-automated-extraction-of-visual-acuity-data-from-free-text-unstructured-clinical-records
#6
Douglas M Baughman, Grace L Su, Irena Tsui, Cecilia S Lee, Aaron Y Lee
PURPOSE: With increasing volumes of electronic health record data, algorithm-driven extraction may aid manual extraction. Visual acuity often is extracted manually in vision research. The total visual acuity extraction algorithm (TOVA) is presented and validated for automated extraction of visual acuity from free text, unstructured clinical notes. METHODS: Consecutive inpatient ophthalmology notes over an 8-year period from the University of Washington healthcare system in Seattle, WA were used for validation of TOVA...
March 2017: Translational Vision Science & Technology
https://www.readbyqxmd.com/read/28293864/using-probabilistic-record-linkage-of-structured-and-unstructured-data-to-identify-duplicate-cases-in-spontaneous-adverse-event-reporting-systems
#7
Kory Kreimeyer, David Menschik, Scott Winiecki, Wendy Paul, Faith Barash, Emily Jane Woo, Meghna Alimchandani, Deepa Arya, Craig Zinderman, Richard Forshee, Taxiarchis Botsis
INTRODUCTION: Duplicate case reports in spontaneous adverse event reporting systems pose a challenge for medical reviewers to efficiently perform individual and aggregate safety analyses. Duplicate cases can bias data mining by generating spurious signals of disproportional reporting of product-adverse event pairs. OBJECTIVE: We have developed a probabilistic record linkage algorithm for identifying duplicate cases in the US Vaccine Adverse Event Reporting System (VAERS) and the US Food and Drug Administration Adverse Event Reporting System (FAERS)...
March 14, 2017: Drug Safety: An International Journal of Medical Toxicology and Drug Experience
https://www.readbyqxmd.com/read/28281211/bayesian-molecular-design-with-a-chemical-language-model
#8
Hisaki Ikebata, Kenta Hongo, Tetsu Isomura, Ryo Maezono, Ryo Yoshida
The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement...
March 9, 2017: Journal of Computer-aided Molecular Design
https://www.readbyqxmd.com/read/28270190/social-media-for-arthritis-related-comparative-effectiveness-and-safety-research-and-the-impact-of-direct-to-consumer-advertising
#9
Jeffrey R Curtis, Lang Chen, Phillip Higginbotham, W Benjamin Nowell, Ronit Gal-Levy, James Willig, Monika Safford, Joseph Coe, Kaitlin O'Hara, Roee Sa'adon
BACKGROUND: Social media may complement traditional data sources to answer comparative effectiveness/safety questions after medication licensure. METHODS: The Treato platform was used to analyze all publicly available social media data including Facebook, blogs, and discussion boards for posts mentioning inflammatory arthritis (e.g. rheumatoid, psoriatic). Safety events were self-reported by patients and mapped to medical ontologies, resolving synonyms. Disease and symptom-related treatment indications were manually redacted...
March 7, 2017: Arthritis Research & Therapy
https://www.readbyqxmd.com/read/28268855/the-effects-of-deep-network-topology-on-mortality-prediction
#10
Hao Du, Mohammad M Ghassemi, Mengling Feng
Deep learning has achieved remarkable results in the areas of computer vision, speech recognition, natural language processing and most recently, even playing Go. The application of deep-learning to problems in healthcare, however, has gained attention only in recent years, and it's ultimate place at the bedside remains a topic of skeptical discussion. While there is a growing academic interest in the application of Machine Learning (ML) techniques to clinical problems, many in the clinical community see little incentive to upgrade from simpler methods, such as logistic regression, to deep learning...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268838/visualizing-patient-journals-by-combining-vital-signs-monitoring-and-natural-language-processing
#11
Adnan Vilic, John Asger Petersen, Karsten Hoppe, Helge B D Sorensen
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268720/s2ni-a-mobile-platform-for-nutrition-monitoring-from-spoken-data
#12
Niloofar Hezarjaribi, Cody A Reynolds, Drew T Miller, Naomi Chaytor, Hassan Ghasemzadeh
Diet and physical activity are important lifestyle and behavioral factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used in the past to objectively measure physical activity or detect eating time. Diet monitoring, however, still relies on self-recorded data by end users where individuals use mobile devices for recording nutrition intake by either entering text or taking images. Such approaches have shown low adherence in technology adoption and achieve only moderate accuracy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28267590/unsupervised-ensemble-ranking-of-terms-in-electronic-health-record-notes-based-on-their-importance-to-patients
#13
Jinying Chen, Hong Yu
BACKGROUND: Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, EHR notes contain abundant medical jargon that can be difficult for patients to comprehend. One way to help patients is to reduce information overload and help them focus on medical terms that matter most to them. Targeted education can then be developed to improve patient EHR comprehension and the quality of care...
March 3, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28257498/text-mining-for-improved-exposure-assessment
#14
Kristin Larsson, Simon Baker, Ilona Silins, Yufan Guo, Ulla Stenius, Anna Korhonen, Marika Berglund
Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure information available from web-based databases, such as PubMed, is undoubtedly a great asset to the scientific community. However, manual retrieval of relevant published information is an extremely time consuming task and overviewing the data is nearly impossible. Here, we present the development of an automatic classifier for chemical exposure information...
2017: PloS One
https://www.readbyqxmd.com/read/28254237/prediction-of-advertisement-preference-by-fusing-eeg-response-and-sentiment-analysis
#15
Himaanshu Gauba, Pradeep Kumar, Partha Pratim Roy, Priyanka Singh, Debi Prosad Dogra, Balasubramanian Raman
This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video...
February 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28254090/detecting-negation-and-scope-in-chinese-clinical-notes-using-character-and-word-embedding
#16
Tian Kang, Shaodian Zhang, Nanfang Xu, Dong Wen, Xingting Zhang, Jianbo Lei
BACKGROUND AND OBJECTIVES: Researchers have developed effective methods to index free-text clinical notes into structured database, in which negation detection is a critical but challenging step. In Chinese clinical records, negation detection is particularly challenging because it may depend on upstream Chinese information processing components such as word segmentation [1]. Traditionally, negation detection was carried out mostly using rule-based methods, whose comprehensiveness and portability were usually limited...
March 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28253531/big-data-analyses-in-health-and-opportunities-for-research-in-radiology
#17
Yindalon Aphinyanaphongs
This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data repositories available for research. This article aims to invoke novel research ideas through a combination of examples of analyses and domain knowledge.
February 2017: Seminars in Musculoskeletal Radiology
https://www.readbyqxmd.com/read/28241760/early-recognition-of-multiple-sclerosis-using-natural-language-processing-of-the-electronic-health-record
#18
Herbert S Chase, Lindsey R Mitrani, Gabriel G Lu, Dominick J Fulgieri
BACKGROUND: Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers. METHODS: An MS-enriched cohort of patients with well-established MS (nā€‰=ā€‰165) and controls (nā€‰=ā€‰545), was generated from the adult outpatient clinic...
February 28, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28227053/the-effects-of-deep-network-topology-on-mortality-prediction
#19
Hao Du, Mohammad M Ghassemi, Mengling Feng, Hao Du, Mohammad M Ghassemi, Mengling Feng, Mengling Feng, Hao Du, Mohammad M Ghassemi
Deep learning has achieved remarkable results in the areas of computer vision, speech recognition, natural language processing and most recently, even playing Go. The application of deep-learning to problems in healthcare, however, has gained attention only in recent years, and it's ultimate place at the bedside remains a topic of skeptical discussion. While there is a growing academic interest in the application of Machine Learning (ML) techniques to clinical problems, many in the clinical community see little incentive to upgrade from simpler methods, such as logistic regression, to deep learning...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227035/visualizing-patient-journals-by-combining-vital-signs-monitoring-and-natural-language-processing
#20
Adnan Vilic, John Asger Petersen, Karsten Hoppe, Helge B D Sorensen, Adnan Vilic, John Asger Petersen, Karsten Hoppe, Helge B D Sorensen, John Asger Petersen, Adnan Vilic, Karsten Hoppe, Helge B D Sorensen
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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