Relevant bibliographies by topics / Signal detection in pharmacovigilance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Signal detection in pharmacovigilance'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Signal detection in pharmacovigilance"

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Kumar, Anoop, and Henna Khan. "Signal Detection and their Assessment in Pharmacovigilance." Open Pharmaceutical Sciences Journal 2, no. 1 (2015): 66–73. http://dx.doi.org/10.2174/1874844901502010066.

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Signal detection and its assessment is the most important aspect in pharmacovigilance which plays a key role in ensuring that patients receive safe drugs. For detection of adverse drug reactions, clinical trials usually provide limited information as they are conducted under strictly controlled conditions. Some of the adverse drug reactions can be detected only after long term use in larger population and in specific patient groups due to specific concomitant medications or disease. The detection of unknown and unexpected safety signals as early as possible from post marketing data is one of t
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V. Sriramakrishnan, G., M. Muthu Selvam, K. Mariappan, and G. Suseendran. "Pharmacovigilance, signal detection using statistical data mining methods." International Journal of Engineering & Technology 7, no. 2.31 (2018): 122. http://dx.doi.org/10.14419/ijet.v7i2.31.13423.

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Pharmacovigilance programmes monitor and help safeguarding the use of medicines which is grave to the success of public health programmes. Identifying new possible risks and developing risk minimization action plans to prevent or ease these risks is at the heart of all pharmacovigilance activities throughout the product lifecycle. In this paper we examine the use of data mining algorithms to identify signals from adverse events reported. The capabilities include screening, data mining and frequency tabulation for potential signals, including signal estimation using established statistical sign
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Rani, Ritu, Subhash Chand, Arjun Singh, Deovrat Kumar, and Meena Devi. "A Review on Signal in Pharmacovigilance." Asian Journal of Pharmaceutical Research and Development 7, no. 6 (2019): 80–84. http://dx.doi.org/10.22270/ajprd.v7i6.591.

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A safety signal is data or information that may suggest a new causal association, or contribute new information about a known association, between a medicine and an adverse event that justifies further investigation. Signals are generated from several sources such as spontaneous reports, clinical studies and the scientific literature. Signal is a potential medicine safety issues, which are derived from individual case safety reports in vigiBase. Signal detection is a cornerstone of drug development process ensuring drug safety. The early detection of Signals helps to improve patient safety and
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Meyboom, Ronald H. B., Antoine C. G. Egberts, I. Ralph Edwards, Yechiel A. Hekster, Fred H. P. de Koning, and Frank W. J. Gribnau. "Principles of Signal Detection in Pharmacovigilance." Drug Safety 16, no. 6 (1997): 355–65. http://dx.doi.org/10.2165/00002018-199716060-00002.

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Shakir, Saad A. W. "Thoughts on Signal Detection in Pharmacovigilance." Drug Safety 30, no. 7 (2007): 603–6. http://dx.doi.org/10.2165/00002018-200730070-00005.

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Noguchi, Yoshihiro, Tomoya Tachi, and Hitomi Teramachi. "Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database." Pharmaceutics 12, no. 8 (2020): 762. http://dx.doi.org/10.3390/pharmaceutics12080762.

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Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Even
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Lee, Seung-Mi, Seokyung Hahn, and Byung-Joo Park. "Signal Detection and Causality Evaluation for Pharmacovigilance." Journal of Korean Society for Clinical Pharmacology and Therapeutics 13, no. 2 (2005): 121. http://dx.doi.org/10.12793/jkscpt.2005.13.2.121.

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Sorbello, Alfred, Anna Ripple, Joseph Tonning, et al. "Harnessing scientific literature reports for pharmacovigilance." Applied Clinical Informatics 26, no. 01 (2017): 291–305. http://dx.doi.org/10.4338/aci-2016-11-ra-0188.

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Summary Objectives: We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. Methods: A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We
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Sharma, Rohit, Jayram Hazra, Rohit K. Ravte, and Arunabh Tripathi. "Pharmacovigilance in Ayurveda: Statistical Input for Signal Detection." Journal of Drug Research in Ayurvedic Sciences 4, no. 1 (2019): 33–38. http://dx.doi.org/10.5005/jdras-10059-0061.

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Meyboom, R. H. B., A. C. G. Egberts, and I. R. Edwards. "Erratum to: Principles of signal detection in pharmacovigilance." Drug Safety 17, no. 2 (1997): 118. http://dx.doi.org/10.1007/bf03257469.

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Dissertations / Theses on the topic "Signal detection in pharmacovigilance"

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Perry, Michelle P. A. "Combining quantitative and qualitative methods in signal detection and evaluation in pharmacovigilance." Thesis, University of Portsmouth, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.618567.

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Background: Pharmacovigilance (PV) is the science and activities involved in monitoring and developing the safety profile of all marketed medicines. Adverse drug reactions (ADRs) for medicinal products can be identified through postmarketing studies by methods of signal detection. Traditional, qualitative methods involve clinical review of cases, and coupled with modem, quantitative methods which have evolved as PV has grown, may help surveillance of the large number of medicinal products on the market today. This research aimed to investigate combining traditional and modern methods of signal
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Bate, Andrew. "The use of Bayesian confidence propagation neural network in pharmacovigilance." Doctoral thesis, Umeå University, Pharmacology and Clinical Neuroscience, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-83.

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<p>The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously."</p><p>The analysis of such a large amount of data on a case by
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Courtois, Émeline. "Score de propension en grande dimension et régression pénalisée pour la détection automatisée de signaux en pharmacovigilance Propensity Score-Based Approaches in High Dimension for Pharmacovigilance Signal Detection: an Empirical Comparison on the French Spontaneous Reporting Database New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASR009.

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La pharmacovigilance a pour but de détecter le plus précocement possible les effets indésirables des médicaments commercialisés. Elle repose sur l’exploitation de grandes bases de données de notifications spontanées, c’est-à-dire de cas rapportés par des professionnels de santé d’évènements indésirables soupçonnées d’être d’origine médicamenteuse. L’exploitation automatique de ces données pour l’identification de signaux statistiques repose classiquement sur des méthodes de disproportionnalité qui s’appuient sur la forme agrégée des données. Plus récemment, des méthodes basées sur des régressi
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Saunders, Gary University of Ballarat. "Pharmacovigilance Decision Support: The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology." University of Ballarat, 2006. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12755.

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The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that
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Saunders, Gary. "Pharmacovigilance Decision Support: The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology." University of Ballarat, 2006. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15382.

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The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that
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Pham, Minh H. "Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods." Scholar Commons, 2018. http://scholarcommons.usf.edu/etd/7218.

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One of the objectives of the U.S. Food and Drug Administration is to protect the public health through post-marketing drug safety surveillance, also known as Pharmacovigilance. An inexpensive and efficient method to inspect post-marketing drug safety is to use data mining algorithms on electronic health records to discover associations between drugs and adverse events. The purpose of this study is two-fold. First, we review the methods and algorithms proposed in the literature for identifying association drug interactions to an adverse event and discuss their advantages and drawbacks. Second,
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Hedenmalm, Karin. "Hazards of Drug Therapy : On the Management of Adverse Drug Reactions: From Signal Detection and Evaluation to Risk Minimization." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5866.

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Zhang, Yi. "Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

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Strandell, Johanna. "Drug interaction surveillance using individual case safety reports." Doctoral thesis, Linköpings universitet, Klinisk farmakologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70424.

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Background: Drug interactions resulting in adverse drug reactions (ADRs) represent a major health problem both for individuals and society in general. Post-marketing pharmacovigilance reporting databases with compiled individual case safety reports (ICSRs) have been shown to be particularly useful in the detection of novel drug - ADR combinations, though these reports have not been fully used to detect adverse drug interactions. Aim: To explore the potential to identify drug interactions using ICSRs and to develop a method to facilitate the detection of adverse drug interaction signals in the
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Arnaud, Mickael. "Développement d’un système automatisé de détection et de priorisation des signaux de sécurité adapté aux bases de données médico-administratives." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0715/document.

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L’amélioration de la surveillance de la sécurité des médicaments grâce à l’utilisation des bases de données médico-administratives est source de nombreuses promesses. L’objectif de cette thèse était de développer un système automatisé de détection et de priorisation des signaux de sécurité adapté aux bases de données médico-administratives, pour la surveillance de la sécurité des médicaments des maladies chroniques. Les antidiabétiques non insuliniques (ANI) ont été sélectionnés comme cas d’étude pour le développement et l’évaluation de ce système. Nous avons d’abord étudié le profil d’utilisa
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Books on the topic "Signal detection in pharmacovigilance"

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Council for International Organizations of Medical Sciences. Working Group VIII. Practical aspects of signal detection in pharmacovigilance: Report of CIOMS Working Group VIII. CIOMS, 2010.

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Tuzlukov, V. P. Signal Detection Theory. Birkhäuser Boston, 2001.

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Tuzlukov, Vyacheslav P. Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8.

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Signal detection and estimation. 2nd ed. Artech House, 2005.

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Barkat, Mourad. Signal detection and estimation. Artech House, 1991.

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Elementary signal detection theory. Oxford University Press, 2002.

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Song, Iickho. Advanced Theory of Signal Detection: Weak Signal Detection in Generalized Observations. Springer Berlin Heidelberg, 2002.

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Jinsoo, Bae, and Ki Sun Yong 1968-, eds. Advanced theory of signal detection: Weak signal detection in generalized observations. Springer, 2002.

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Song, Iickho, Jinsoo Bae, and Sun Yong Kim. Advanced Theory of Signal Detection. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04859-7.

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Hart, G. F. Wind propeller signal detection improvements. Tennessee Applied Physics, Inc., 1992.

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Book chapters on the topic "Signal detection in pharmacovigilance"

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Bate, Andrew, Antoine Pariente, Manfred Hauben, and Bernard Bégaud. "Quantitative Signal Detection and Analysis in Pharmacovigilance." In Mann's Pharmacovigilance. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118820186.ch20.

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Leufkens, Hubert G., and Antoine C. Egberts. "Pharmacovigilance: from signal to action." In Clinical Nephrotoxins. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-84843-3_5.

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Elsner, James B., and Anastasios A. Tsonis. "Signal Detection." In Singular Spectrum Analysis. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2514-8_7.

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Evans, David C. "Signal Detection." In Bottlenecks. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2580-6_8.

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Nahler, Gerhard. "signal detection." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1283.

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Bendall, C. "Legal Aspects of Pharmacovigilance." In Stephens' Detection of New Adverse Drug Reactions. John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470014199.ch10.

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Arnold, B. D. C. "Regulatory Aspects of Pharmacovigilance." In Stephens' Detection of New Adverse Drug Reactions. John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470014199.ch9.

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Strutt, Kristina Leila, and Barry David Charles Arnold. "Regulatory Aspects of Pharmacovigilance." In Stephens' Detection and Evaluation of Adverse Drug Reactions. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9780470975053.ch8.

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Robinson, Michael. "Detection." In Topological Signal Processing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36104-3_4.

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Brown, E. G. "Dictionaries and Coding in Pharmacovigilance." In Stephens' Detection of New Adverse Drug Reactions. John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470014199.ch12.

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Conference papers on the topic "Signal detection in pharmacovigilance"

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Revol, Bruno, Ingrid Jullian-Desayes, Jean-Luc Cracowki, et al. "Gabapentinoids and sleep apnea syndrome: a safety signal from the WHO pharmacovigilance database." In ERS/ESRS Sleep and Breathing Conference 2019 abstracts. European Respiratory Society, 2019. http://dx.doi.org/10.1183/23120541.sleepandbreathing-2019.p151.

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Pépin, J. L., B. Revol, I. Jullian-Desayes, et al. "Gabapentinoids and Sleep Apnea Syndrome: A Safety Signal from the Who Pharmacovigilance Database." In American Thoracic Society 2019 International Conference, May 17-22, 2019 - Dallas, TX. American Thoracic Society, 2019. http://dx.doi.org/10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a2868.

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Santos, Paulo, Patricia Oliveira, Beatriz Kaippert, et al. "Current challenge in pharmacovigilance at Bio-Manguinhos/Fiocruz: detection of drug related problems in social media." In III Seminário Anual Científico e Tecnológico de Bio-Manguinhos. Instituto de Tecnologia em Imunobiológicos, 2016. http://dx.doi.org/10.35259/isi.sact.2016_28298.

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Cunha, Alexandre M. R., Kele T. Belloze, and Gustavo P. Guedes. "Recognizing pharmacovigilance named entities in Brazilian Portuguese with CoreNLP." In XIII Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/bresci.2019.6314.

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Textual data sources may assist in the detection of adverse events not predicted for a particular drug. However, given the amount of information available in several sources, it is reasonable to adopt a computational approach to analyze these sources to search for adverse events. In this scenario, we created an extension of CoreNLP to process Brazilian Portuguese texts from pharma- covigilance area. We trained three natural language models: a Part-of-speech tagger, a parser and a Named Entity Recognizer. Preliminary results indicate success in generating a dependency tree for phrases in the ph
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Lei, Zhongding, and Francois Chin. "WiMax signal detection." In MILCOM 2008 - 2008 IEEE Military Communications Conference (MILCOM). IEEE, 2008. http://dx.doi.org/10.1109/milcom.2008.4753616.

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Somasundaram, S. D., A. Jakobsson, and E. Gudmundson. "Robust NQR Signal Detection." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366784.

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Barrett, H. H., K. J. Myers, and R. F. Wagner. "Beyond Signal-Detection Theory." In Application of Optical Instrumentation in Medicine XIV and Picture Archiving and Communication Systems (PACS IV) for Medical Applications, edited by Samuel J. Dwyer III and Roger H. Schneider. SPIE, 1986. http://dx.doi.org/10.1117/12.975398.

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Liubun, Zinovii, Vasyl Mandziy, Hans Klein, Oleksandr Karpin, and Vasyl Rabyk. "Hover Signal-Profile Detection." In 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2020. http://dx.doi.org/10.1109/csit49958.2020.9322027.

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Kodipaka, Surya, Ajaya Dahal, Logan Smith, et al. "Adversarial indoor signal detection." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2021. http://dx.doi.org/10.1117/12.2587525.

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Lin, Ong Chun, Agus T. Kwee, and Flora S. Tsai. "Database optimization for novelty detection." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397520.

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Reports on the topic "Signal detection in pharmacovigilance"

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Broder, Bruce, and Stuart Schwartz. Quickest Detection Procedures and Transient Signal Detection. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230068.

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Hughes, Timothy M. A Signal Energy Detection Implementation. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada372823.

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Baker, C. R., M. R. Frey, and A. F. Gualtierotti. Some Results on Nongaussian Signal Detection. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada207255.

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Rao, C. R. Some Recent Results in Signal Detection. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177197.

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TEXAS UNIV AT AUSTIN APPLIED RESEARCH LABS. Continuation of Signal Detection Using Polyspectra. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada280176.

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Schlesinger, M. E., and T. P. Barnett. On greenhouse gas signal detection strategies. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6282370.

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Forrest, Robert. Convolutional Neural Networks for Signal Detection. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1813655.

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VALLEY, MICHAEL T., BRUCE D. HANSCHE, THOMAS L. PAEZ, ANGEL URBINA, and DENNIS M. ASHBAUGH. Advanced Signal Processing for Thermal Flaw Detection. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/787641.

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Nielsen, P. A., and John Thomas. Signal Detection in Arctic Under-Ice Noise. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada204175.

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Forrest, R. N. Active Sonar Detection and Signal Excess Fluctuations. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada200932.

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