Predictive Analytics for Real-Time Compliance Monitoring and Gap Analysis in GxP-Regulated Systems
Keywords:
predictive analytics, compliance monitoring, gap analysis, GxP regulationsAbstract
Predictive analytics is turned out to be a revolutionary approach for ensuring real time compliance monitoring and automated gap analysis in GxP-regulated environments. The aim of this study is to present a predictive analytics model that can utilise real time data stream to proactively identify and encounter compliance risks within Quality Management Systems (QMS) such as SAP SuccessFactors and Veeva.
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References
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2020.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
C. Dwork and A. Roth, "The Algorithmic Foundations of Differential Privacy," Foundations and Trends in Theoretical Computer Science, vol. 9, no. 3–4, pp. 211–407, 2014.
J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint, arXiv:1804.02767, 2018.
A. Vaswani et al., "Attention Is All You Need," in Proc. 31st Conf. Neural Inf. Process. Syst. (NeurIPS), Long Beach, CA, USA, 2017, pp. 5998–6008.
Y. Lecun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, 2015.
A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder, Bitcoin and Cryptocurrency Technologies. Princeton, NJ, USA: Princeton Univ. Press, 2016.
K. Kannan and B. Arunachalam, "AI-Driven Regulatory Compliance: A Review of Machine Learning-Based Compliance Monitoring Systems," IEEE Trans. Comput. Soc. Syst., vol. 8, no. 4, pp. 1020–1033, Dec. 2021.
M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, "Disease Prediction by Machine Learning Over Big Data From Healthcare Communities," IEEE Access, vol. 5, pp. 8869–8879, May 2017.
A. Ahmad, R. Qamar, and M. Qasim, "A Blockchain-Based Framework for Transparent and Immutable Regulatory Compliance Monitoring," in Proc. IEEE Int. Conf. Blockchain (Blockchain-IEEE), 2022, pp. 325–332.
D. Silver et al., "Mastering the Game of Go with Deep Neural Networks and Tree Search," Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016.
P. Smuha, "The Ethics of AI: A Critical Review of European AI Regulation and Its Implications for Compliance Automation," IEEE Technol. Soc. Mag., vol. 40, no. 2, pp. 36–46, June 2021.
N. Carlini, D. Wagner, and I. Goodfellow, "Adversarial Machine Learning and Its Implications for Regulatory Compliance Monitoring," IEEE Secur. Priv., vol. 19, no. 1, pp. 19–28, Jan.–Feb. 2021.
M. Brundage et al., "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation," AI & Soc., vol. 34, no. 2, pp. 341–359, June 2019.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, "Membership Inference Attacks Against Machine Learning Models," in Proc. 2017 IEEE Symp. Secur. Privacy (SP), 2017, pp. 3–18.
Y. Zhang, X. Chen, and K. Liang, "Federated Learning for Privacy-Preserving Compliance Risk Prediction," in Proc. 2021 IEEE Int. Conf. Data Eng. (ICDE), 2021, pp. 654–663.
J. Konečný et al., "Federated Learning: Strategies for Improving Communication Efficiency," arXiv preprint, arXiv:1610.05492, 2017.
G. Hinton, S. Osindero, and Y. Teh, "A Fast Learning Algorithm for Deep Belief Nets," Neural Comput., vol. 18, no. 7, pp. 1527–1554, July 2006.
S. Arunkumar, "Natural Language Processing for Automated Compliance Risk Assessment in Pharmaceutical Industries," IEEE Access, vol. 10, pp. 14863–14878, 2022.
L. Breiman, "Random Forests," Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.