1,640
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Quality medical data management within an open AI architecture – cancer patients case

ORCID Icon, ORCID Icon, &
Article: 2194581 | Received 20 Dec 2022, Accepted 19 Mar 2023, Published online: 13 Apr 2023

References

  • Alanazi, A. (2022). Using machine learning for healthcare challenges and opportunities. Informatics in Medicine Unlocked, 30, paper 10092430. https://doi.org/10.1016/j.imu.2022.100924
  • Autexier, S., Lüth, C., & Drechsler, R. (2022). Chapter the Bremen ambient assisted living Lab and beyond – smart environments, smart services and artificial intelligence in medicine for humans. In M. A. Pfannstiel (Ed.), Künstliche Intelligenz im Gesundheitswesen (German Title: Das Bremen Ambient Assisted Living Lab und darüber hinaus – Intelligente Umgebungen, smarte Services und Künstliche Intelligenz in der Medizin für den Menschen). Springer Fachmedien Verlag Wiesbaden, March. pp 835–850, https://doi.org/10.1007/978-3-658-33597-7_40
  • Burmester, G. R. (2018). Rheumatology 4.0: Big data, wearables and diagnosis by computer. Annals of the Rheumatic Diseases, 77(7), 963–965. https://doi.org/10.1136/annrheumdis-2017-212888
  • Chida, Y., Hamer, M., Wardle, J., & Steptoe, A. (2008). Do stress-related psychosocial factors contribute to cancer incidence and survival? Nature Clinical Practice Oncology, 5(8), 466–475. https://doi.org/10.1038/ncponc1134. Epub 2008 May 20. PMID: 18493231.
  • Claeys, A., & Vialatte, J. S. (2014). Les progrès de la génétique: versune médecine de précision? Les enjeux scientifiques, technologiques, sociaux et éthiques de la médecine personnalisée [Advances in genetics: Towards a precision medicine? Technological, social and ethical scientific issues of personalised medicine].
  • Cohen, S., Murphy, M. L. M., & Prather, A. A. (2019). Ten surprising facts about stressful life events and disease risk. Annual Review of Psychology, 70(1), 577–597. https://doi.org/10.1146/annurev-psych-010418-102857. Epub 2018 Jun 27. PMID: 29949726; PMCID: PMC6996482.
  • (Deliverable D1.1). ASCAPE Deliverable – D1.1 Positioning ASCAPE’s open Al infrastructure in the after cancer-care Iron Triangle of Health. https://ascapeproject.eu/node/57
  • (Deliverable D2.3) D2.3. System architecture. https://www.bd2decide.eu/deliverables/d23-system-architecture
  • (Deliverable D2.4). ASCAPE Deliverable – D2.4 ML-DL Training and Evaluation Report. https://ascape-project.eu/node/118
  • (Deliverable D4.1). ASCAPE Deliverable – D4.1 Personalized interventions and user-centric visualizations. https://ascape-project.eu/node/120
  • (Deliverable D5.1) D5.1. Multilayer data acquisition and management services. https://www.bd2decide.eu/deliverables/d51-multilayer-data-acquisition-and-management-services
  • Fan, Z. Y., Yang, Y., Zhang, C. H., Yin, R. Y., Tang, L., & Zhang, F. (2021). Prevalence and patterns of comorbidity among middle-aged and elderly people in China: A cross-sectional study based on CHARLS data. International Journal of General Medicine, 14, 1449–1455. https://doi.org/10.2147/IJGM.S309783
  • Ficek, J., Wang, W., Chen, H., Dagne, G., & Daley, E. (2021). Differential privacy in health research: A scoping review. Journal of the American Medical Informatics Association, 28(10), 2269–2276. https://doi.org/10.1093/jamia/ocab135
  • Gallos, P., Aso, S., Autexier, S., Brotons, A., De Nigro, A., Jurak, G., Kiourtis, A., Kranas, P., Kyriazis, D., Lustrek, M. & Magdalinou, A. (2019). CrowdHEALTH: Big data analytics and holistic health records. Studies in Health Technology and Informatics, 258, 255–256. PMID: 30942764.
  • He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0
  • Hiremath, S., Yang, G., & Mankodiya, K. (2014). Wearable internet of things: Concept, architectural components and promises for person-centered healthcare. In 2014 4th International Conference on Wireless Mobile Communication and Healthcare-Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH), pp. 304–307, IEEE.
  • Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2022). Explainable AI methods-a brief overview. In A. Holzinger, R. Goebel, R. Fong, T. Moon, K. R. Müller, W. Samek (Eds.), xxAI - Beyond Explainable AI (pp. 13–38, Vol. 13200). xxAI 2020. Lecture Notes in Computer Science. Springer.  https://doi.org/10.1007/978-3-031-04083-2_2
  • Ilić, M., Ivanović, M., Jakovetić, D., Kurbalija, V., Otlokan, M., Savić, M., & Vujnović-Sedlar, N. (2023). ASCAPE – An intelligent approach to support cancer patients. WorldCist'23 – 11st World Conference on Information Systems and Technologies, to be held in Pisa, Italy, 4–6 April 2023.
  • Ivanovic, M., Autexier, S., & Kokkonidis, M. (2022). AI approaches in processing and using data in personalized medicine. In S. Chiusano, T. Cerquitelli, & R. Wrembel (Eds.), Advances in databases and information systems. ADBIS 2022. Lecture notes in computer science (Vol. 13389, pp. 11– 24). Springer. https://doi.org/10.1007/978-3-031-15740-0_2
  • Ivanovic, M., & Balaz, I. Influence of artificial intelligence on personalized medical predictions, interventions and quality of life issues. ICSTCC 2020 In 24th International Conference on System Theory, Control and Computing, ICSTCC 2020, Sinaia, Romania, IEEE 2020, ISBN 978-1-7281-9809-5:445450.
  • Ivanović, M., & Ninković, S. (2017). Personalized HealthCare and agent technologies. 11th KES International Symposium on Agent and Multi-Agent Systems, Technologies and Applications, Vilamoura, Portugal, 21-23 June, pp. 3-11, Springer.
  • Ji, Z., Lipton, Z. C., & Elkan, C., (2014). Differential privacy and machine Learning: A survey and review. arXiv:1412.7584 [cs.LG]. https://doi.org/10.48550/arXiv.1412.7584
  • Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, M. A. N., Bonawitz, K., Charles, G., Cormode, Z., Cummings, R., D'Oliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., … Jag, M. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977.
  • Kaissis, G. A., Makowski, M. R., Rückert, D., Braren Rickmer F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305–311. https://doi.org/10.1038/s42256-020-0186-1
  • Kalloniatis, C., Lambrinoudakis, C., Musahl, M., Kanatas, A., & Gritzalis, S. (2021). Incorporating privacy by design in body sensor networks for medical applications: A privacy and data protection framework. Computer Science and Information Systems, 18(1), 323–350. https://doi.org/10.2298/CSIS200922057K
  • Kingston, A., Robinson, L., Booth, H., Knapp, M., Jagger, C., & for the MODEM project (May 2018). Projections of multi-morbidity in the older population in England to 2035: Estimates from the population ageing and care simulation (PACSim) model. Age and Ageing, 47(3), 374–380. https://doi.org/10.1093/ageing/afx201
  • Kumar, S., Rana, M., Verma, K., Singh, N., Sharma, A., Maria, A., Singh, G., Khaira, H., & Saini, S. (2014). Prediqt-cx: Post treatment health related quality of life prediction model for cervical cancer patients. PLoS One 9(2), e89851. https://doi.org/10.1371/journal.pone.0089851
  • Kyriazis, D., Autexier, S., Brondino, I., Boniface, M., Donat, L., Engen, V., Fernandez, R., Jimenez-Peris, R., Jordan, B. & Jurak, G. (2017). Crowdhealth: Holistic health records and big data analytics for health policy making and personalized health. Informatics Empowers Healthcare Transformation, 238, 19–23. https://doi.org/10.3233/978-1-61499-781-8-19
  • Lahiri, C., Pawar, S., & Mishra, R. (2019). Precision medicine and future of cancer treatment. Precision Cancer Medicine, 2, 33, AME Publishing. https://doi.org/10.21037/pcm.2019.09.01
  • Lampropoulos, K., Kosmidis, T., Autexier, S., Savic, M., Athanatos, M., Kokkonidis, M., Koutsouri, T., Vizitiu, A., Valachis, A., & Padron, M. Q. ASCAPE: An open AI ecosystem to support the quality of life of cancer patients. In Proceedings of ICHI 2021 – 9th IEEE International Conference on Healthcare Informatics, pp. 301–310.
  • Lee, D., Cornet, R., Lau, F., & De Keizer, N. (2013). A survey of SNOMED CT implementations. Journal of Biomedical Informatics, 46(1), 87–96. https://doi.org/10.1016/j.jbi.2012.09.006
  • Lee, L. H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., Kumar A., Bermejo C., & Hui, P. (2021). All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda. arXiv preprint arXiv:2110.05352.
  • (Link 1). H2020 project. https://www.bd4qol.eu/wps/portal/site/big-data-for-quality-of-life
  • (Link 2). https://www.england.nhs.uk/cancer/living/
  • (Link 3). Follow-up medical care. https://www.cancer.gov/about-cancer/coping/survivorship/follow-up-care
  • (Link 4). Healthcare classifications and terminologies. https://guides.library.kumc.edu/c.php?g=451743&p=3084407
  • (Link 5). Healthcare, artificial intelligence, top 6 challenges of AI in healthcare & how to overcome them. https://research.aimultiple.com/challenges-of-ai-in-healthcare/
  • Lloret, J., Canovas, A., Sendra, S., & Parra, L. (2015). A smart communication architecture for ambient assisted living. IEEE Communications Magazine, 53(1), 26–33. https://doi.org/10.1109/MCOM.2015.7010512
  • Norbye, A., Abelsen, B., Førde, O., & Ringberg, U. (2022). Distribution of health anxiety in a general adult population and associations with demographic and social network characteristics. Psychological Medicine, 52(12), 2255–2262. https://doi.org/10.1017/S0033291720004122
  • Park, J., & Lim, H. (2022). Privacy-preserving federated learning using homomorphic encryption. Applied Sciences, 12(2), 734. https://doi.org/10.3390/app12020734
  • Saadat, S., Aziz, A., Ahmad, H., Imtiaz, H., Sohail, Z., Kazmi, A., Aslam, S., Naqvi, N., & Saadat, S. (2017). Predicting quality of life changes in hemodialysis patients using machine learning: Generation of an early warning system. Cureus, 9. https://doi.org/10.7759/cureus.1713
  • Salih, A., & Abraham, A. (2016). Ambient intelligence assisted healthcare monitoring (p. 192). LAP LAMBERT Academic Publishing.
  • Savić, M., Kurbalija, V., Ilić, M., Ivanović, M., Jakovetić, D., Valachis, A., Autexier, S., Rust, J., & Kosmidis, T. (2023). The application of machine learning techniques in prediction of quality of life features for cancer patients. Computer Science and Information Systems, 20(1), 381–404. https://doi.org/10.2298/CSIS220227061S
  • Schulz, S., Stegwee, R., & Chronaki, C. (2019). Standards in healthcare data. In P. Kubben, M. Dumontier, & A. Dekker (Eds.), Fundamentals of clinical data science. Springer. https://doi.org/10.1007/978-3-319-99713-1_3
  • Siddique, M., Mirza, M. A., Ahmad, M., Chaudhry, J., & Islam, R. (2018). A survey of big data security solutions in healthcare. In International Conference on Security and Privacy in Communication Systems, pp. 391–406. Springer, Cham.
  • Sim, J., Kim, Y., Kim, J., Lee, J., Kim, M. S., Shim, Y., Zo, J., & Yun, Y. H. (2020). The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: Applications of machine learning. Scientific Reports, 10(1), Article 10693. https://doi.org/10.1038/s41598-020-67604-3
  • Sinha, R., & Heuvel, W. (06 2011). A systematic literature review of quality of life in lower limb amputees. Disability and Rehabilitation, 33(11), 883–899. https://doi.org/10.3109/09638288.2010.514646
  • Tyler, N. S., Mosquera-Lopez, C. M., Wilson, L. M., Dodier, R. H., Branigan, D. L., Gabo, V. B., Guillot, F. H., Hilts, W. W., El Youssef, J., Castle, J. R., & Jacobs, P. G. (2020). An artificial intelligence decision support system for the management of type 1 diabetes. Nature Metabolism, 2(7), 612–619. https://doi.org/10.1038/s42255-020-0212-y
  • Tzelves, L., Manolitsis, I., Varkarakis, I., Ivanovic, M., Kokkonidis, M., Useros, C. S., Kosmidis, T., Muñoz, M., Grau, I., Athanatos, M., Vizitiu, A., Lampropoulos, K., Koutsouri, T., Stefanatou, D., Perrakis, K., Stratigaki, C., Autexier, S., Kosmidis, P., & Valachis, A. (2022). Artificial intelligence supporting cancer patients across Europe – The ASCAPE project. PLoS One, 17(4), e0265127. https://doi.org/10.1371/journal.pone.0265127
  • Venne, J., Busshoff, U., Poschadel, S., Menschel, R., Evangelatos, N., Vysyaraju, K., & Brand, A. (2020). International consortium for personalized medicine: An international survey about the future of personalized medicine. Personalized Medicine, 17(2), 89–100. https://doi.org/10.2217/pme-2019-0093
  • Vizitiu, A., Nita, C. I., Puiu, A., Suciu, C., & Itu, L. M. (2020). Applying deep neural networks over homomorphic encrypted medical data. Computational and Mathematical Methods in Medicine, 2020, 3910250:1–3910250:26. https://doi.org/10.1155/2020/3910250
  • Wu, M., & Luo, J. (2019). Wearable technology applications in healthcare: A literature review. The Online Journal of Nursing Informatics, 23(3). https://www.himss.org/resources/wearable-technology-applications-healthcare-literature-review
  • Yang, Z., Olszewski, D., He, C., Pintea, G., Lian, J., Chou, T., Chen, R. C., & Shtylla, B. (2021). Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy. Computers in Biology and Medicine, 129, 104127. https://doi.org/10.1016/j.compbiomed.2020.104127