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Journal of Biomedical Informatics

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https://www.readbyqxmd.com/read/28729030/natural-language-processing-systems-for-capturing-and-standardizing-unstructured-clinical-information-a-systematic-review
#1
REVIEW
Kory Kreimeyer, Matthew Foster, Abhishek Pandey, Nina Arya, Gwendolyn Halford, Sandra F Jones, Richard Forshee, Mark Walderhaug, Taxiarchis Botsis
We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers...
July 17, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28723580/quality-assurance-of-chemical-ingredient-classification-for-the-national-drug-file-reference-terminology
#2
Ling Zheng, Hasan Yumak, Ling Chen, Christopher Ochs, James Geller, Joan Kapusnik-Uner, Yehoshua Perl
The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies...
July 16, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28723579/elucidating-high-dimensional-cancer-hallmark-annotation-via-enriched-ontology
#3
Shankai Yan, Ka-Chun Wong
MOTIVATION: Cancer hallmark annotation is a promising technique that could discover novel knowledge about cancer from the biomedical literature. The automated annotation of cancer hallmarks could reveal relevant cancer transformation processes in the literature or extract the articles that correspond to the cancer hallmark of interest. It acts as a complementary approach that can retrieve knowledge from massive text information, advancing numerous focused studies in cancer research. Nonetheless, the high-dimensional nature of cancer hallmark annotation imposes a unique challenge...
July 16, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28720438/on-the-utility-of-3d-hand-cursors-to-explore-medical-volume-datasets-with-a-touchless-interface
#4
Daniel Simões Lopes, Pedro Duarte de Figueiredo Parreira, Soraia Figueiredo Paulo, Vitor Nunes, Paulo Amaral Rego, Manuel Cassiano Neves, Pedro Silva Rodrigues, Joaquim Armando Jorge
Analyzing medical volume datasets requires interactive visualization so that users can extract anatomo-physiological information in real-time. Conventional volume rendering systems rely on 2D input devices, such as mice and keyboards, which are known to hamper 3D analysis as users often struggle to obtain the desired orientation that is only achieved after several attempts. In this paper, we address which 3D analysis tools are better performed with 3D hand cursors operating on a touchless interface comparatively to a 2D input devices running on a conventional WIMP interface...
July 15, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28712748/chi-a-contemporaneous-health-index-for-degenerative-disease-monitoring-using-longitudinal-measurements
#5
Yijun Huang, Qiang Meng, Heather Evans, William Lober, Yu Cheng, Xiaoning Qian, Ji Liu, Shuai Huang
In this paper, we develop a novel formulation for contemporaneous patient risk monitoring by exploiting the emerging data-rich environment in many healthcare applications, where an abundance of longitudinal data that reflect the degeneration of the health condition can be continuously collected. Our objective, and the developed formulation, is fundamentally different from many existing risk score models for different healthcare applications, which mostly focus on predicting the likelihood of a certain outcome at a pre-specified time...
July 13, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28711679/reproducibility-of-studies-on-text-mining-for-citation-screening-in-systematic-reviews-evaluation-and-checklist
#6
Babatunde Kazeem Olorisade, Pearl Brereton, Peter Andras
CONTEXT: Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields...
July 12, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28709856/developing-smartphone-apps-for-behavioural-studies-the-alcorisk-app-case-study
#7
Anthony Smith, Kristy de Salas, Ian Lewis, Benjamin Schüz
Advances in mobile technology and significantly increasing utilization of mobile devices such as smartphones and tablets have resulted in a paradigm shift from PC-centric computing to mobile computing. The results of careful analysis conducted of this mobile landscape indicate that there is a growing demand for smart, user-centric, situation-aware mobile software. Invariably, concomitant with this demand is the need for methodologies that can provide support the development of this type of software. In this paper, we propose a semantic framework, called the mobile situation-aware framework, which supports efficient modeling, construction, processing, management, and inference of mobile situation information...
July 11, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28694119/recurrent-neural-networks-for-classifying-relations-in-clinical-notes
#8
Yuan Luo
We proposed the first models based on recurrent neural networks (more specifically Long Short-Term Memory - LSTM) for classifying relations from clinical notes. We tested our models on the i2b2/VA relation classification challenge dataset. We showed that our segment LSTM model, with only word embedding feature and no manual feature engineering, achieved a micro-averaged f-measure of 0.661 for classifying medical problem-treatment relations, 0.800 for medical problem-test relations, and 0.683 for medical problem-medical problem relations...
July 7, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28694118/automatic-classification-of-rdoc-positive-valence-severity-with-a-neural-network
#9
Cheryl Clark, Ben Wellner, Rachel Davis, John Aberdeen, Lynette Hirschman
OBJECTIVE: Our objective was to develop a machine learning-based system to determine the severity of Positive Valance symptoms for a patient, based on information included in their initial psychiatric evaluation. Severity was rated on an ordinal scale of 0-3 as follows: 0 (absent=no symptoms), 1 (mild=modest significance), 2 (moderate=requires treatment), 3 (severe=causes substantial impairment) by experts. MATERIALS AND METHODS: We treated the task of assigning Positive Valence severity as a text classification problem...
July 7, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28690054/fuzzy-evidential-network-and-its-application-as-medical-prognosis-and-diagnosis-models
#10
Amin Janghorbani, Mohammad Hassan Moradi
Uncertainty is one of the important facts of the medical knowledge. Medical prognosis and diagnosis, as the essential parts of medical knowledge, is affected by different aspects of uncertainty, which must be managed. In the previous studies, different theories such as Bayesian probability theory, evidence theory, and fuzzy set theory have been developed to represent and manage different aspects of uncertainty. Recently, hybrid frameworks are suggested to deal with various types of uncertainty in a single framework...
July 6, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28687199/leveraging-syntax-to-better-capture-the-semantics-of-elliptical-coordinated-compound-noun-phrases
#11
Catherine Blake, Tom Rindflesch
Full-text scientific articles are increasingly available, but capturing the meaning conveyed within an article automatically remains a bottleneck for semantic search and reasoning systems. In this paper we consider elliptical coordinated compound noun phrases that authors use to save space in an article. Systems that do not attend to coordination would incorrectly interpret "breast or lung cancer" as a body part (breast) and a disease (lung cancer) rather than two diseases. The algorithmic approach introduced in this paper uses a generate-and-test strategy where candidate expansions for forward, backward and complex ellipses are generated from syntactic dependencies...
July 4, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28684255/prescription-extraction-using-crfs-and-word-embeddings
#12
Carson Tao, Michele Filannino, Özlem Uzuner
In medical practices, doctors detail patients' care plan via discharge summaries written in the form of unstructured free texts, which among the others contain medication names and prescription information. Extracting prescriptions from discharge summaries is challenging due to the way these documents are written. Handwritten rules and medical gazetteers have proven to be useful for this purpose but come with limitations on performance, scalability, and generalizability. We instead present a machine learning approach to extract and organize medication names and prescription information into individual entries...
July 3, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28676255/an-ontology-based-approach-to-patient-follow-up-assessment-for-continuous-and-personalized-chronic-disease-management
#13
Yi-Fan Zhang, Ling Gou, Tian-Shu Zhou, De-Nan Lin, Jing Zheng, Ye Li, Jing-Song Li
OBJECTIVE: Chronic diseases are complex and persistent clinical conditions that require close collaboration among patients and health care providers in the implementation of long-term and integrated care programs. However, current solutions focus partially on intensive interventions at hospitals rather than on continuous and personalized chronic disease management. This study aims to fill this gap by providing computerized clinical decision support during follow-up assessments of chronically ill patients at home...
July 1, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28663073/a-study-of-the-suitability-of-autoencoders-for-preprocessing-data-in-breast-cancer-experimentation
#14
Laura Macías-García, José María Luna-Romera, Jorge García-Gutiérrez, María Del Mar Martínez-Ballesteros, José C Riquelme-Santos, Ricardo González-Cámpora
Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes involved in breast cancer can be identified by immunohistochemistry. However, this method has problems arising from the intra-observer and inter-observer variability in the assessment of pathologic variables, which may result in misleading conclusions. Using an optimal selection of preprocessing techniques may help to reduce observer variability. Deep learning has emerged as a powerful technique for any tasks related to machine learning such as classification and regression...
June 26, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28663072/automatic-prediction-of-coronary-artery-disease-from-clinical-narratives
#15
Kevin Buchan, Michele Filannino, Özlem Uzuner
Coronary Artery Disease (CAD) is not only the most common form of heart disease, but also the leading cause of death in both men and women[1]. We present a system that is able to automatically predict whether patients develop coronary artery disease based on their narrative medical histories, i.e., clinical free text. Although the free text in medical records has been used in several studies for identifying risk factors of coronary artery disease, to the best of our knowledge our work marks the first attempt at automatically predicting development of CAD...
June 26, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28648605/a-semi-supervised-approach-using-label-propagation-to-support-citation-screening
#16
Georgios Kontonatsios, Austin J Brockmeier, Piotr Przybyła, John McNaught, Tingting Mu, John Y Goulermas, Sophia Ananiadou
Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance...
June 22, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28625880/predicting-biomedical-metadata-in-cedar-a-study-of-gene-expression-omnibus-geo
#17
Maryam Panahiazar, Michel Dumontier, Olivier Gevaert
A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1,3 million GEO records...
June 15, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28624644/psychiatric-symptom-recognition-without-labeled-data-using-distributional-representations-of-phrases-and-on-line-knowledge
#18
Yaoyun Zhang, Olivia Zhang, Yonghui Wu, Hee-Jin Lee, Jun Xu, Hua Xu, Kirk Roberts
OBJECTIVE: Mental health is becoming an increasingly important topic in healthcare. Psychiatric symptoms, which consist of subjective descriptions of the patient's experience, as well as the nature and severity of mental disorders, are critical to support the phenotypic classification for personalized prevention, diagnosis, and intervention of mental disorders. However, few automated approaches have been proposed to extract psychiatric symptoms from clinical text, mainly due to (a) the lack of annotated corpora, which are time-consuming and costly to build, and (b) the inherent linguistic difficulties that symptoms present as they are not well-defined clinical concepts like diseases...
June 15, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28624642/drugsemantics-a-corpus-for-named-entity-recognition-in-spanish-summaries-of-product-characteristics
#19
Isabel Moreno, Ester Boldrini, Paloma Moreda, M Teresa Romá-Ferri
For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care. The problem is that such information is stored in different sources and their consultation time is limited. In this context, Natural Language Processing techniques can be applied to efficiently transform textual data into structured information so that it could be used in critical healthcare applications, being of help for physicians in their daily workload, such as: decision support systems, cohort identification, patient management, etc...
June 14, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28624641/text-mining-applied-to-electronic-cardiovascular-procedure-reports-to-identify-patients-with-trileaflet-aortic-stenosis-and-coronary-artery-disease
#20
Aeron M Small, Daniel H Kiss, Yevgeny Zlatsin, David L Birtwell, Heather Williams, Marie A Guerraty, Yuchi Han, Saif Anwaruddin, John H Holmes, Julio A Chirinos, Robert L Wilensky, Jay Giri, Daniel J Rader
BACKGROUND: Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest...
June 14, 2017: Journal of Biomedical Informatics
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