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Test-Driven Anonymization in Health Data: A Case Study on Assistive Reproduction

Cristian Augusto, Miguel Angel Olivero, Jesus Moran, Leticia Morales, Claudio de la Riva, Javier Aroba, Javier Tuya
2020 2020 IEEE International Conference On Artificial Intelligence Testing (AITest)  
To obtain a trade-off between both qualities (privacy and functional suitability), we use the Test-Driven Anonymization (TDA) approach, which anonymizes incrementally the data to train the AI tools and  ...  Artificial intelligence (AI) is a broad field whose prevalence in the health sector has increased during recent years.  ...  Augusto et al., "Test-Driven Anonymization in Health Data: A Case Study on Assistive Reproduction," 2020 IEEE International Conference On Artificial Intelligence Testing (AITest), Oxford, United Kingdom  ... 
doi:10.1109/aitest49225.2020.00019 dblp:conf/aitest/AugustoOMMRAT20 fatcat:m73v5j66hfgodh4zsmmywoyffy

Table of Contents

2019 2019 IEEE International Conference On Artificial Intelligence Testing (AITest)  
Anonymization for Artificial Intelligence 103 Cristian Augusto (University of Oviedo), Jesús Morán (University of Oviedo), Claudio De La Riva (University of Oviedo), and Javier Tuya (University of  ...  Session: Automated Testing and AI User-Assisted Log Analysis for Quality Control of Distributed Fintech Applications ATARI: Autonomous Testing and Real-Time Intelligence -A Framework for Autonomously Testing  ... 
doi:10.1109/aitest.2019.00004 fatcat:ijwo6plmzbf27hka4qiij3bygq

Special focus on artificial intelligence for optical communications

Yuefeng Ji, Chao Lu, Darko Zibar, Huanlai Xing
2020 Science China Information Sciences  
Special focus on artificial intelligence for optical communications * Artificial intelligence (AI) and its related techniques bring new vitality and provide new approaches to optical communications, and  ...  testing  ...  Besides, we express our deepest gratitude to all the anonymous reviewers for delivering high-quality and timely review comments.  ... 
doi:10.1007/s11432-020-2898-2 fatcat:jpth3h4ly5dyvjmzqrgcqit66u

Table of Contents

2020 2020 IEEE International Conference On Artificial Intelligence Testing (AITest)  
University), Ian Bayley (Oxford Brookes University), Dongmei Liu (Nanjing University of Science and Technology), and Xiaoyu Zheng (Nanjing University of Science and Technology) Test-Driven Anonymization  ...  ), Naveen Ravipati (San Jose State University), and Magdalini Eirinaki (San Jose State University) ML for Testing (I) Identifying and Generating Missing Tests using Machine Learning on Execution Traces  ... 
doi:10.1109/aitest49225.2020.00004 fatcat:ysnjkasjbnau7dzmvpuc464qki

Artificial Intelligent Credit Risk Prediction: An Empirical Study of Analytical Artificial Intelligence Tools for Credit Risk Prediction in a Digital Era

2019 Journal of Accounting and Finance  
The research describes three experiments that develop the artificial intelligent probability of default models. In all experiments AI models performed better than the traditional models.  ...  The CAGR for digital lending is 53% until 2025.  ...  ACKNOWLEDGMENTS Manuel Martin Ortiz for performing analyses. Tomas Garcia for data and modeling support.  ... 
doi:10.33423/jaf.v19i8.2622 fatcat:khuavhv5pzegho44oeoedjy2pe

ENVISION – Improve intensive care of COVID-19 patients with artificial intelligence

Alpo Olavi Värri, Antti Kallonen, Hannu Nieminen, Mark Van Gils
2021 Finnish Journal of eHealth and eWelfare  
Twelve European hospitals participate in the collection of patient data for the development and validation of the artificial intelligence tools.  ...  The Envision project aims at developing artificial intelligence-based tools for supporting the treatment of critically ill COVID-19 patients in the intensive care unit.  ...  The main focus of the Envision project is to improve the intensive care of the COVID-19 patients by supporting it with artificial intelligence (AI).  ... 
doi:10.23996/fjhw.109929 fatcat:msggxrffcjf7ld7u5idb4dbbsy

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Tanja B. Jutzi, Eva I. Krieghoff-Henning, Tim Holland-Letz, Jochen Sven Utikal, Axel Hauschild, Dirk Schadendorf, Wiebke Sondermann, Stefan Fröhling, Achim Hekler, Max Schmitt, Roman C. Maron, Titus J. Brinker
2020 Frontiers in Medicine  
About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for  ...  The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios.  ...  V. and Astrid Doppler and Katharina Kaminski of Melanom-Info Germany for distribution of the questionnaire.  ... 
doi:10.3389/fmed.2020.00233 pmid:32671078 pmcid:PMC7326111 fatcat:5de2yegxvrgzrmlb3krcgyac2m

Artificial Intelligence‐Based Clinical Decision Support for COVID‐19–Where Art Thou?

Mathias Unberath, Kimia Ghobadi, Scott Levin, Jeremiah Hinson, Gregory D. Hager
2020 Advanced Intelligent Systems  
Although artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date.  ...  In this perspective piece, the opportunities and requirements for AI-based clinical decision support systems are identified and challenges that impact "AI readiness" for rapidly emergent healthcare challenges  ...  This article was supported by institutional funds provided by the Malone Center for Engineering in Healthcare.  ... 
doi:10.1002/aisy.202000104 pmid:32838300 pmcid:PMC7361146 fatcat:gv5y7jfmxjcv5msh5e2hgfsrjm

Shoreline: Data-Driven Threshold Estimation of Online Reserves of Cryptocurrency Trading Platforms (Student Abstract)

Xitong Zhang, He Zhu, Jiayu Zhou
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge of crypto-exchanges is asset safety, and all-time amount hacked from crypto-exchanges until 2018 is over $1.5 billion  ...  There are two major components included as Copyright c 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.  ...  Following (Mahdavi, Khoshraftar, and An 2018) , we apply the link prediction task for evaluation. The AUC scores of all testing snapshots are shown in Table 1 .  ... 
doi:10.1609/aaai.v34i10.7265 fatcat:4jdwgl7o6zaedoaj4motjfq5ru

Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence

H. S. Jennath, V S Anoop, S Asharaf
2020 International Journal of Interactive Multimedia and Artificial Intelligence  
This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed.  ...  This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients.  ...  more equitable development of Artificial Intelligence and Artificial General Intelligence.  ... 
doi:10.9781/ijimai.2020.07.002 fatcat:4pendnhr6rd3jd2dz7ns5ke4qa

Editorial for Advances in Intelligent Mobile Applications Special Issue

Jordán Pascual Espada, Ronald R. Yager, Antonio J. Jara
2016 Journal on spesial topics in mobile networks and applications  
This special issue features seven selected papers with high quality related to intelligent mobile applications. The papers included in this special issue have been selected among 30 papers.  ...  The first paper, BA POI-aware Power Saving Scheme for Ubiquitous Touring Service using Mobile Devices over the Cellular and Wi-Fi Hybrid Network^, authored by Chao-Hsien Lee, Chung-Ming Huang and Wei-Shuang  ...  The system defines a Complex Shape Histogram and an artificial intelligence engine, used for classifying 3D point clouds with Support Vector Machine.  ... 
doi:10.1007/s11036-016-0697-5 fatcat:ztelgjvaercuzpvuynpxqwb65u

Artificial Intelligence-based Clinical Decision Support for COVID-19 – Where Art Thou? [article]

Mathias Unberath and Kimia Ghobadi and Scott Levin and Jeremiah Hinson and Gregory D Hager
2020 arXiv   pre-print
While artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date.  ...  In this perspective piece, we identify opportunities and requirements for AI-based clinical decision support systems and highlight challenges that impact "AI readiness" for rapidly emergent healthcare  ...  Acknowledgements This article was supported by institutional funds provided by the Malone Center for Engineering in Healthcare. References  ... 
arXiv:2006.03434v1 fatcat:nivjn5k27zb2hkjtaxuh5u42gu

Mobile Artificial Intelligence Technology for Detecting Macula Edema and Subretinal Fluid on OCT Scans: Initial Results from the DATUM alpha Study [article]

Stephen G. Odaibo, Mikelson MomPremier, Richard Y. Hwang, Salman J. Yousuf, Steven L. Williams, Joshua Grant
2019 arXiv   pre-print
Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease.  ...  Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally.  ...  Scott for kindly reviewing the draft and providing helpful suggestions.  ... 
arXiv:1902.02905v2 fatcat:digjpr7tyzajrptuqniinstmoa

Data-driving methods: More than merely trendy buzzwords?

Julien Textoris, Fabio Silvio Taccone, Lara Zafrani, Antoine Guillon, Sébastien Gibot, Fabrice Uhel, Eric Azabou, Guillaume Monneret, Frédéric Pène, Nicolas de Prost, Stein Silva
2018 Annals of Intensive Care  
It is worth noting that this approach does not look for causality and simply aim to detect significant data configurations Machine learning Derived methods from artificial intelligence that provides computers  ...  A growing body of research has recently suggested that emerging artificial intelligence (AI)-derived methods could help physicians to access, organize and use important amounts of data more easily.  ...  Additionally, hypothesis-driven methods are built on optimised models derived from artificial intelligence domains, which can learn and evolve without explicit programming, and validate the created model  ... 
doi:10.1186/s13613-018-0405-7 pmid:29721786 pmcid:PMC5931952 fatcat:3diudqoptnay5obpyysgmsrdk4

Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction

Julanar Ahmed Fadhil, Koh Tieng Wei, Kew Si Na
2020 Journal of Computer Science  
Thus, several Artificial Intelligence (AI) techniques have been introduced that are intensively used in the modern software engineering industry to fulfill market needs.  ...  The evidence showed that the software engineering domain has utilized artificial intelligence approaches and techniques to facilitate the complex tasks of software bug detection and bug prediction.  ...  The authors would like to thank the editors and all anonymous reviewers for valuable comments.  ... 
doi:10.3844/jcssp.2020.1709.1717 fatcat:v3jj33ch6veanlli6anhswhh5y
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