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Sharing Biomedical Data: Strengthening AI Development in Healthcare

Tania Pereira, Joana Morgado, Francisco Silva, Michele M. Pelter, Vasco Rosa Dias, Rita Barros, Cláudia Freitas, Eduardo Negrão, Beatriz Flor de Lima, Miguel Correia da Silva, António J. Madureira, Isabel Ramos (+4 others)
2021 Healthcare  
Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed  ...  However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical  ...  Synthetic Data Generative models have recently been applied to generate augmented data for biomedical datasets, thus emerging as a useful tool to increase the number and variability of available examples  ... 
doi:10.3390/healthcare9070827 fatcat:e7jjlalkevfgbh24swnidl26ze

D2.7 Key performance indicators selection and definition - final version

Susanna Bonura, Davide Dalle Carbonare
2020 Zenodo  
The Evaluation Framework employed for validating the MUSKETEER platform, is based on the Goal Question Metric (GQM) method.  ...  Starting from this final version of the MUSKETEER evaluation framework and KPIs, data collection and interpretation phases will be documented in the deliverables D7.5 and D7.6, where the description of  ...  For a single hospital it is very complicate to collect a dataset large enough to create a complex predictive model.  ... 
doi:10.5281/zenodo.5845684 fatcat:zedfi6jdibgptbherd6rp2abte

MUSKETEER D2.3 Key performance indicators selection and definition

Susanna Bonura, Davide Dalle Carbonare
2020 Zenodo  
The Evaluation Framework employed for validating the MUSKETEER platform, is based on the Goal Question Metric (GQM) method.  ...  This document describes the planning and the definition phases, which have been achieved at this stage of the project. A revision of the KPIs and methodology will be done in M24.  ...  For a single hospital it is very complicate to collect a dataset large enough to create a complex predictive model.  ... 
doi:10.5281/zenodo.4730055 fatcat:qksagw5sffasnn7xairvylyqm4

Biomedical Informatics on the Cloud

Peipei Ping, Henning Hermjakob, Jennifer S. Polson, Panagiotis V. Benos, Wei Wang
2018 Circulation Research  
A unified computational platform leverages metadata to not only provide direction but also empower researchers to mine a wealth of biomedical information and forge novel mechanistic insights.  ...  In the digital age of cardiovascular medicine, the rate of biomedical discovery can be greatly accelerated by the guidance and resources required to unearth potential collections of knowledge.  ...  , for their contributions regarding Reactome enlisted pathways and other related components.  ... 
doi:10.1161/circresaha.117.310967 pmid:29700073 pmcid:PMC6192708 fatcat:kekqc3gegvcc3hm56xzw3cosmu

Special issue on privacy and security on pervasive e-health and assistive environments

Grammati Pantziou, Fillia Makedon, Petros Belsis
2011 Security and Communication Networks  
With her team, she has been developing data modeling and data analysis tools with broad impact in biomedical computing and bioinformatics, security, and other areas.  ...  These factors put a resource limitation on implementing resourcedemanding tools and protocols for privacy protection.  ... 
doi:10.1002/sec.318 fatcat:m5ofxoowj5fvdbde2bfwm7bgxi

Poster abstracts from fourth annual public meeting: Mobilizing computable biomedical knowledge (MCBK 2021)

2021 Learning Health Systems  
W3C-leveraging commodity tools for representation and reasoning purposes.  ...  For each category of knowledge artifacts, the knowledge can be curated, distributed, and executed using different industry standards-usually published by organizations such as OMG, HL7, OASIS, and the  ...  While research is important, the preservation of patient privacy needs to be built into institutional models for expanding research using real-world data.  ... 
doi:10.1002/lrh2.10300 fatcat:kibvalnyljgcrlfwfik3wqgfbe

The cost of quality: Implementing generalization and suppression for anonymizing biomedical data with minimal information loss

Florian Kohlmayer, Fabian Prasser, Klaus A. Kuhn
2015 Journal of Biomedical Informatics  
Finally, we evaluate our solution with multiple datasets and privacy models.  ...  Objective: With the ARX data anonymization tool structured biomedical data can be de-identified using syntactic privacy models, such as k-anonymity.  ...  utility, (2) the first comprehensive study of monotonicity of common privacy criteria and utility measures within a coding model that uses generalization and suppression, (3) an experimental evaluation  ... 
doi:10.1016/j.jbi.2015.09.007 pmid:26385376 fatcat:wbuhw2dmqfh4jcxubtbkeminp4

MIRACUM: Medical Informatics in Research and Care in University Medicine

Till Acker, Johannes Bernarding, Harald Binder, Martin Boeker, Melanie Boerries, Philipp Daumke, Thomas Ganslandt, Jürgen Hesser, Gunther Höning, Michael Neumaier, Kurt Marquardt, Harald Renz (+9 others)
2018 Methods of Information in Medicine  
Objectives: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system.  ...  predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards.  ...  Acknowledgment We are grateful to the many members of the MIRACUM team who have actively participated in the design and first implementation of the described architecture and data integration centers.  ... 
doi:10.3414/me17-02-0025 pmid:30016814 pmcid:PMC6178200 fatcat:5vgcr2psinbbdgkolpsdapqxee

Ontology-Enhanced Interactive Anonymization in Domain-Driven Data Mining Outsourcing

Brian C.S. Loh, Patrick H.H. Then
2010 2010 Second International Symposium on Data, Privacy, and E-Commerce  
The research objective is to create an ontology-based constrained anonymization framework which aims to preserve meaningful and actionable models for domain-driven data mining while protecting privacy.  ...  Two sets of models were created for each parameter, one with attribute constraints and the other without. Next, remaining attributes in the models were analyzed and compared.  ...  Data Privacy and Protection PPDP involves the development of methods or tools for releasing data that remains practically useful, while still preserving individual privacy.  ... 
doi:10.1109/isdpe.2010.7 fatcat:euaht3j43rcoxkmaqda6bjsqi4

Privacy-preserving Artificial Intelligence Techniques in Biomedicine [article]

Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Späth, Nina Kerstin Wenke, Béla Bihari, Tobias Frisch, Anne Hartebrodt, Anne-Christin Hausschild (+13 others)
2020 arXiv   pre-print
This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications.  ...  This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress.  ...  Figure 1 and Figure 2 have been created with BioRender.com.  ... 
arXiv:2007.11621v2 fatcat:qnmzqvqn5fgonjiwmudjlzwelm

Data Integration Challenges for Machine Learning in Precision Medicine

Mireya Martínez-García, Enrique Hernández-Lemus
2022 Frontiers in Medicine  
In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes.  ...  Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants.  ...  EH-L and MM-G performed research and drafted the manuscript. All authors reviewed and approved the manuscript.  ... 
doi:10.3389/fmed.2021.784455 pmid:35145977 pmcid:PMC8821900 fatcat:nxco4r4gsjdfnpbgoditxjptya

Secondary use of clinical data: The Vanderbilt approach

Ioana Danciu, James D. Cowan, Melissa Basford, Xiaoming Wang, Alexander Saip, Susan Osgood, Jana Shirey-Rice, Jacqueline Kirby, Paul A. Harris
2014 Journal of Biomedical Informatics  
This work provides a summary of our approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others  ...  The last decade has seen an exponential growth in the quantity of clinical data collected nationwide, triggering an increase in opportunities to reuse the data for biomedical research.  ...  Dario Giuse; the Enterprise Data Warehouse, lead architect Eric Griffin; the Biomedical Language Processing Lab, led by Dr. Joshua Denny; the Health Information Privacy Lab led by Dr.  ... 
doi:10.1016/j.jbi.2014.02.003 pmid:24534443 pmcid:PMC4133331 fatcat:y6i3uq4u3rdhhp5mp2fpq5fue4

Personalized medicine: challenges and opportunities for translational bioinformatics

Casey Lynnette Overby, Peter Tarczy-Hornoch
2013 Personalized Medicine  
Personalized medicine can be defined broadly as a model of healthcare that is predictive, personalized, preventive and participatory.  ...  to optimize the transformation of increasing voluminous biomedical data into proactive, predictive, preventative and participatory health."  ...  P Tarczy-Hornoch was supported in part by CTSA (NIH NCRR 1 UL1 RR 025014), WA State Life Sciences Discover Fund grant 'Northwest Institute for Genetic Medicine' and CSER (NIH NHGRI U01 HG006507).  ... 
doi:10.2217/pme.13.30 pmid:24039624 pmcid:PMC3770190 fatcat:6bafa4ln6jcdtdnjirdipstyui

The Effect of Exposure to Social Annotation on Online Informed Consent Beliefs and Behavior

Martina Balestra, Orit Shaer, Johanna Okerlund, Madeleine Ball, Oded Nov
2016 Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16  
that the effect of the exposure to social annotation was stronger among users characterized by relatively lower levels of prior privacy preserving behaviors.  ...  In this controlled between-subjects experiment, participants were presented with an online consent form for a personal genomics study.  ...  ACKNOWLEDGMENTS This work was partially funded by National Science Foundation grants IIS-1017693 and IIS-1422706.  ... 
doi:10.1145/2818048.2820012 dblp:conf/cscw/BalestraSOBN16 fatcat:qivdwoi7nzffvoqfy2aqesox4e

Deep learning for healthcare: review, opportunities and challenges

Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, Joel T. Dudley
2017 Briefings in Bioinformatics  
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care.  ...  However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists.  ...  DeepCare was evaluated for disease progression modeling, intervention recommendation and future risk prediction on diabetes and mental health patient cohorts.  ... 
doi:10.1093/bib/bbx044 pmid:28481991 fatcat:oefjv547ivazzoal3qc77d7ti4
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