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"Natural Language Processing"

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https://www.readbyqxmd.com/read/29352548/prediction-of-psychosis-across-protocols-and-risk-cohorts-using-automated-language-analysis
#1
Cheryl M Corcoran, Facundo Carrillo, Diego Fernández-Slezak, Gillinder Bedi, Casimir Klim, Daniel C Javitt, Carrie E Bearden, Guillermo A Cecchi
Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e...
February 2018: World Psychiatry: Official Journal of the World Psychiatric Association (WPA)
https://www.readbyqxmd.com/read/29346583/datamed-an-open-source-discovery-index-for-finding-biomedical-datasets
#2
Xiaoling Chen, Anupama E Gururaj, Burak Ozyurt, Ruiling Liu, Ergin Soysal, Trevor Cohen, Firat Tiryaki, Yueling Li, Nansu Zong, Min Jiang, Deevakar Rogith, Mandana Salimi, Hyeon-Eui Kim, Philippe Rocca-Serra, Alejandra Gonzalez-Beltran, Claudiu Farcas, Todd Johnson, Ron Margolis, George Alter, Susanna-Assunta Sansone, Ian M Fore, Lucila Ohno-Machado, Jeffrey S Grethe, Hua Xu
Objective: Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. Materials and Methods: DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium...
January 13, 2018: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29346410/an-evaluation-of-multi-probe-locality-sensitive-hashing-for-computing-similarities-over-web-scale-query-logs
#3
Graham Cormode, Anirban Dasgupta, Amit Goyal, Chi Hoon Lee
Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users' queries from commercial search engines), computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH) methods and evaluate four variants in a distributed computing environment (specifically, Hadoop)...
2018: PloS One
https://www.readbyqxmd.com/read/29335238/automating-quality-measures-for-heart-failure-using-natural-language-processing-a-descriptive-study-in-the-department-of-veterans-affairs
#4
Jennifer Hornung Garvin, Youngjun Kim, Glenn Temple Gobbel, Michael E Matheny, Andrew Redd, Bruce E Bray, Paul Heidenreich, Dan Bolton, Julia Heavirland, Natalie Kelly, Ruth Reeves, Megha Kalsy, Mary Kane Goldstein, Stephane M Meystre
BACKGROUND: We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. OBJECTIVE: To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. METHODS: We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications...
January 15, 2018: JMIR Medical Informatics
https://www.readbyqxmd.com/read/29329456/designing-and-evaluating-an-automated-system-for-real-time-medication-administration-error-detection-in-a-neonatal-intensive-care-unit
#5
Yizhao Ni, Todd Lingren, Eric S Hall, Matthew Leonard, Kristin Melton, Eric S Kirkendall
Background: Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Methods: Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period...
January 10, 2018: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29321958/diagnostic-value-of-strand-specific-mirna-101-3p-and-mirna-101-5p-for-hepatocellular-carcinoma-and-a-bioinformatic-analysis-of-their-possible-mechanism-of-action
#6
Xia Yang, Yu-Yan Pang, Rong-Quan He, Peng Lin, Jie-Mei Cen, Hong Yang, Jie Ma, Gang Chen
There is accumulating evidence that miRNA might serve as potential diagnostic and prognostic markers for various types of cancer. Hepatocellular carcinoma (HCC) is the most common type of malignant lesion but the significance of miRNAs in HCC remains largely unknown. The present study aimed to establish the diagnostic value of miR-101-3p/5p in HCC and then further investigate the prospective molecular mechanism via a bioinformatic analysis. First, the miR-101 expression profiles and parallel clinical parameters from 362 HCC patients and 50 adjacent non-HCC tissue samples were downloaded from The Cancer Genome Atlas (TCGA)...
January 2018: FEBS Open Bio
https://www.readbyqxmd.com/read/29311050/detecting-novel-and-emerging-drug-terms-using-natural-language-processing-a-social-media-corpus-study
#7
Sean S Simpson, Nikki Adams, Claudia M Brugman, Thomas J Conners
BACKGROUND: With the rapid development of new psychoactive substances (NPS) and changes in the use of more traditional drugs, it is increasingly difficult for researchers and public health practitioners to keep up with emerging drugs and drug terms. Substance use surveys and diagnostic tools need to be able to ask about substances using the terms that drug users themselves are likely to be using. Analyses of social media may offer new ways for researchers to uncover and track changes in drug terms in near real time...
January 8, 2018: JMIR Public Health and Surveillance
https://www.readbyqxmd.com/read/29309523/protein-classification-using-modified-n-grams-and-skip-grams
#8
S M Ashiqul Islam, Benjamin J Heil, Christopher Michel Kearney, Erich J Baker
Motivation: Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m- NGSG)...
December 22, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29304788/analyzing-hidden-populations-online-topic-emotion-and-social-network-of-hiv-related-users-in-the-largest-chinese-online-community
#9
Chuchu Liu, Xin Lu
BACKGROUND: Traditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community...
January 5, 2018: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/29300701/speech2health-a-mobile-framework-for-monitoring-dietary-composition-from-spoken-data
#10
Niloofar Hezarjaribi, Sepideh Mazrouee, Hassan Ghasemzadeh
Diet and physical activity are known as important lifestyle factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used to measure physical activity or detect eating time. In many intervention studies, however, stringent monitoring of overall dietary composition and energy intake is needed. Currently, such a monitoring relies on self-reported data by either entering text or taking an image that represents food intake. These approaches suffer from limitations such as low adherence in technology adoption and time sensitivity to the diet intake context...
January 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/29298665/analysis-of-microarrays-of-mir-34a-and-its-identification-of-prospective-target-gene-signature-in-hepatocellular-carcinoma
#11
Fang-Hui Ren, Hong Yang, Rong-Quan He, Jing-Ning Lu, Xing-Gu Lin, Hai-Wei Liang, Yi-Wu Dang, Zhen-Bo Feng, Gang Chen, Dian-Zhong Luo
BACKGROUND: Currently, some studies have demonstrated that miR-34a could serve as a suppressor of several cancers including hepatocellular carcinoma (HCC). Previously, we discovered that miR-34a was downregulated in HCC and involved in the tumorigenesis and progression of HCC; however, the mechanism remains unclear. The purpose of this study was to estimate the expression of miR-34a in HCC by applying the microarray profiles and analyzing the predicted targets of miR-34a and their related biological pathways of HCC...
January 3, 2018: BMC Cancer
https://www.readbyqxmd.com/read/29287076/do-neural-nets-learn-statistical-laws-behind-natural-language
#12
Shuntaro Takahashi, Kumiko Tanaka-Ishii
The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language...
2017: PloS One
https://www.readbyqxmd.com/read/29278956/framework-for-infectious-disease-analysis-a-comprehensive-and-integrative-multi-modeling-approach-to-disease-prediction-and-management
#13
Madhav Erraguntla, Josef Zapletal, Mark Lawley
The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment...
December 1, 2017: Health Informatics Journal
https://www.readbyqxmd.com/read/29274047/quantitative-analysis-of-uncertainty-in-medical-reporting-creating-a-standardized-and-objective-methodology
#14
Bruce I Reiner
Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizing and quantifying uncertainty language, which could provide both the report author and reader with context related to the perceived level of diagnostic confidence and accuracy. A number of computerized strategies could be employed in the creation of this analysis including string search, natural language processing and understanding, histogram analysis, topic modeling, and machine learning...
December 22, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29261751/egard-extracting-associations-between-genomic-anomalies-and-drug-responses-from-text
#15
A S M Ashique Mahmood, Shruti Rao, Peter McGarvey, Cathy Wu, Subha Madhavan, K Vijay-Shanker
Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of information, especially those that describe therapeutic implications of biomarkers and therefore relevant for treatment selection...
2017: PloS One
https://www.readbyqxmd.com/read/29239117/deep-omics
#16
Ngoc Hieu Tran, Xianglilan Zhang, Ming Li
Deep learning has revolutionized research in image processing, speech recognition, natural language processing, game playing, and will soon in proteomics and genomics. Through three examples in genomics, protein structure prediction, and proteomics, we demonstrate that deep learning is changing bioinformatics research, shifting from algorithm-centric to data-centric approaches. This article is protected by copyright. All rights reserved.
December 14, 2017: Proteomics
https://www.readbyqxmd.com/read/29229587/myvoice-national-text-message-survey-of-youth-aged-14-to-24-years-study-protocol
#17
Melissa DeJonckheere, Lauren P Nichols, Michelle H Moniz, Kendrin R Sonneville, V G Vinod Vydiswaran, Xinyan Zhao, Timothy C Guetterman, Tammy Chang
BACKGROUND: There has been little progress in adolescent health outcomes in recent decades. Researchers and youth-serving organizations struggle to accurately elicit youth voice and translate youth perspectives into health care policy. OBJECTIVE: Our aim is to describe the protocol of the MyVoice Project, a longitudinal mixed methods study designed to engage youth, particularly those not typically included in research. Text messaging surveys are collected, analyzed, and disseminated in real time to leverage youth perspectives to impact policy...
December 11, 2017: JMIR Research Protocols
https://www.readbyqxmd.com/read/29222076/adverse-drug-event-discovery-using-biomedical-literature-a-big-data-neural-network-adventure
#18
Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig
BACKGROUND: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. OBJECTIVE: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs...
December 8, 2017: JMIR Medical Informatics
https://www.readbyqxmd.com/read/29221286/automatic-lung-rads%C3%A2-classification-with-a-natural-language-processing-system
#19
Sebastian E Beyer, Brady J McKee, Shawn M Regis, Andrea B McKee, Sebastian Flacke, Gilan El Saadawi, Christoph Wald
Background: Our aim was to train a natural language processing (NLP) algorithm to capture imaging characteristics of lung nodules reported in a structured CT report and suggest the applicable Lung-RADS™ (LR) category. Methods: Our study included structured, clinical reports of consecutive CT lung screening (CTLS) exams performed from 08/2014 to 08/2015 at an ACR accredited Lung Cancer Screening Center. All patients screened were at high-risk for lung cancer according to the NCCN Guidelines®...
September 2017: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/29218918/annotating-gene-sets-by-mining-large-literature-collections-with-protein-networks
#20
Sheng Wang, Jianzhu Ma, Michael Ku Yu, Fan Zheng, Edward W Huang, Jiawei Han, Jian Peng, Trey Ideker
Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts...
2018: Pacific Symposium on Biocomputing
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