ENHANCING HEALTHCARE AI MODELS WITH SYNTHETIC DATA: SOLUTIONS FOR LIMITED DATA IN DISEASE PREDICTION AND TREATMENT
Keywords:
Synthetic Data, Healthcare AI, Data Privacy, Generative Models, Personalized MedicineAbstract
This article explores the transformative potential of synthetic data in addressing the challenges of limited data availability in healthcare AI development. It examines various techniques for generating synthetic data, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and the Synthetic Minority Over-sampling Technique (SMOTE), and their applications in enhancing disease prediction and treatment optimization models. Through case studies, the article demonstrates how synthetic data can improve rare disease diagnosis, optimize clinical trial design, and enhance predictive models for chronic diseases. The discussion encompasses the strengths of synthetic data in healthcare AI, such as addressing data scarcity and privacy concerns, as well as its limitations, including potential biases and validation challenges. The article concludes by outlining future directions for synthetic data in healthcare, emphasizing its role in advancing personalized medicine and fostering more inclusive and collaborative research environments.
References
J. Walonoski et al., "Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record," Journal of the American Medical Informatics Association, vol. 25, no. 3, pp. 230-238, Mar. 2018. [Online]. Available: https://academic.oup.com/jamia/article/25/3/230/4098271
N. G. Weiskopf and C. Weng, "Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research," Journal of the American Medical Informatics Association, vol. 20, no. 1, pp. 144-151, Jan. 2013. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/22733976/#:~:text=Objective:%20To%20review%20the%20methods%20and
%20dimensions%20of
A. Yale, S. Dash, R. Dutta, I. Guyon, A. Pavao, and K. P. Bennett, "Generation and evaluation of privacy preserving synthetic health data," Neurocomputing, vol. 416, pp. 244-255, Dec. 2020. [Online]. Available: Generation and evaluation of privacy preserving synthetic health data - ScienceDirect
I. Goodfellow et al., "Generative Adversarial Nets," in Advances in Neural Information Processing Systems 27, 2014, pp. 2672–2680. [Online]. Available: https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
A. Abdal, Y. Qin, and P. Wonka, "Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?," in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 4432-4441. [Online]. Available: https://openaccess.thecvf.com/content_ICCV_2019/html/Abdal_Image2StyleGAN_How_to_Embed_Images_Into_the_StyleGAN_Latent_Space_ICCV_2019_paper.html
E. Choi, S. Biswal, B. Malin, J. Duke, W. F. Stewart, and J. Sun, "Generating Multi-label Discrete Patient Records using Generative Adversarial Networks," in Proceedings of the Machine Learning for Healthcare Conference, 2017, pp. 286-305. [Online]. Available: http://proceedings.mlr.press/v68/choi17a.html
H. C. Shin et al., "Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks," in MICCAI Workshop on Simulation and Synthesis in Medical Imaging, 2018, pp. 1-11. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-00536-8_1
J. H. Chen, A. Asch, "Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations," New England Journal of Medicine, vol. 376, no. 26, pp. 2507-2509, 2017. [Online]. Available: https://www.nejm.org/doi/full/10.1056/NEJMp1702071
[9] S. M. Anwar et al., "Medical Image Analysis using Convolutional Neural Networks: A Review," Journal of Medical Systems, vol. 42, no. 11, pp. 1-13, 2018. [Online]. Available: https://link.springer.com/article/10.1007/s10916-018-1088-1
B. K. Beaulieu-Jones et al., "Privacy-preserving generative deep neural networks support clinical data sharing," Circulation: Cardiovascular Quality and Outcomes, vol. 12, no. 7, p. e005122, 2019. [Online]. Available: https://www.ahajournals.org/doi/full/10.1161/CIRCOUTCOMES.118.005122
G. Douzas and F. Bacao, "Effective data generation for imbalanced learning using conditional generative adversarial networks," Science Direct. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0957417417306346#:~:text=Contrary%20to%20these%20algorithms,%20in%20this%20paper%20the
M. Ghassemi, T. Naumann, P. Schulam, A. L. Beam, I. Y. Chen, and R. Ranganath, "A Review of Challenges and Opportunities in Machine Learning for Health," AMIA Summits on Translational Science Proceedings, vol. 2020, pp. 191-200, 2020. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233077/
