TY - GEN
T1 - synple
T2 - 15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024
AU - Silveira, Inês
AU - Silva, Luís
AU - Veladas, Francisco Morgado
AU - Braga, Rodrigo
AU - Gamboa, Hugo
N1 - Funding Information:
This work was supported by the Europe Union\u2019s Horizon Europe research and innovation programme under grant agreement No. 101095387: AISym4Med-Synthetic and Scalable Data Platform for Medical Empowered AI, HORIZON-HLTH-2022-IND-13.
Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024
Y1 - 2024
N2 - Patient data collection is often constrained by accessibility, privacy, and confidentiality issues in healthcare research. To overcome this problem, the generation of synthetic data has been proposed. Nevertheless, existing synthetic patient generators do not cover European populations, nor do they offer flexibility in the process of generating data and building a biographical profile. In this paper, we introduce synple, a tool for synthetic patient data generation that mirrors the demographic characteristics of Portugal and Spain while adhering to GDPR and HIPAA regulations. Our platform produces comprehensive patient profiles, including both biological and biographical details, life narratives, and facial images, facilitated through an intuitive web interface powered by advanced generation algorithms. Additionally, the platform incorporates a validation feature, enabling users to assess the quality and consistency of the generated synthetic data. We demonstrate our system’s potential for enhancing healthcare research and safeguarding patient privacy in the digital age.
AB - Patient data collection is often constrained by accessibility, privacy, and confidentiality issues in healthcare research. To overcome this problem, the generation of synthetic data has been proposed. Nevertheless, existing synthetic patient generators do not cover European populations, nor do they offer flexibility in the process of generating data and building a biographical profile. In this paper, we introduce synple, a tool for synthetic patient data generation that mirrors the demographic characteristics of Portugal and Spain while adhering to GDPR and HIPAA regulations. Our platform produces comprehensive patient profiles, including both biological and biographical details, life narratives, and facial images, facilitated through an intuitive web interface powered by advanced generation algorithms. Additionally, the platform incorporates a validation feature, enabling users to assess the quality and consistency of the generated synthetic data. We demonstrate our system’s potential for enhancing healthcare research and safeguarding patient privacy in the digital age.
KW - artificial intelligence
KW - health data privacy
KW - machine learning
KW - synthetic data
KW - synthetic patient
UR - http://www.scopus.com/inward/record.url?scp=85199605173&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-63851-0_9
DO - 10.1007/978-3-031-63851-0_9
M3 - Conference contribution
AN - SCOPUS:85199605173
SN - 9783031638503
T3 - IFIP Advances in Information and Communication Technology
SP - 139
EP - 151
BT - Technological Innovation for Human-Centric Systems - 15th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Ferrada, Filipa
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 July 2024 through 5 July 2024
ER -