Exploring explainable AI in pharmaceutical decision-making : Bridging the gap between black box models and clinical insights
*V. S. GhorpadeCorresponding authorghorpadevs@gmail.comDepartment of PharmaceuticsKrishna Vishwa Vidyapeeth (Deemed to be University)Krishna Institute of PharmacyKarad, Maharashtra, IndiaView full profile → , Pradnya A. Jadhavja_pradnya@yahoo.comDepartment of Electronics EngineeringYeshwantrao Chavan College of EngineeringNagpur, Maharashtra, IndiaView full profile → , R. S. Jadhavranjitjadhav705@gmail.comDepartment of Pharmaceutical ChemistryKrishna Vishwa Vidyapeeth (Deemed to be University)Krishna Institute of PharmacyKarad, Maharashtra, IndiaView full profile → , Satish N. Gujarsatishgujar@gmail.comDepartment of Computer EngineeringFaculty of EngineeringNavashyandri Education Soc. Group of InstitutePune, Maharashtra, IndiaView full profile → , Wankhede Vishal Ashokwankhedeva@gmail.comDepartment of Electronics and Telecommunication EngineeringS.H.H.J.B. PolytechnicNashik, Maharashtra, IndiaView full profile → , Shraddha V. Panditshraddha.pandit@moderncoe.edu.inDepartment of Artificial Intelligence and Data SciencePES Modern College of EngineeringPune, Maharashtra, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 30 Mar 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1249
- Pages:
- 225–236
Abstract
Keywords
Subject Classifications
References
[1] S. Romdhani, I. Nouaouri, J. Tounsi, H. Allaoui and S. Gattoufi, “Lateral Transshipment in Two-Echelon Inventory Control for Sustainable Pharmaceutical Supply Chain,” 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), Rome, Italy, pp. 2701-2706 (2023).
[2] M. Geisslinger, R. Trauth, G. Kaljavesi and M. Lienkamp, “Maximum Acceptable Risk as Criterion for Decision-Making in Autonomous Vehicle Trajectory Planning,” in IEEE Open Journal of Intelligent Transportation Systems, vol. 4, pp. 570-579 (2023), doi: 10.1109/OJITS.2023.3298973.
[3] J. Han and L. Liu, “Research on tourism attention and emotion analysis of Hongya Cave scenic spot based on Internet big data,” 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, pp. 1055-1059 (2023).
[4] R. Weightman and B. Piccoli, “Optimization of Non-Pharmaceutical Interventions For a Mutating Virus,” 2023 American Control Conference (ACC), San Diego, CA, USA, pp. 307-312 (2023), doi: 10.23919/ACC55779.2023.10156293.
[5] F. Gao et al., “Fine-Grained Relation Extraction for Drug Instructions Using Contrastive Entity Enhancement,” in IEEE Access, vol. 11, pp. 51777-51788 (2023), doi: 10.1109/ACCESS.2023.3279288.
[6] K. -L. A. Yau, Y. -W. Chong, X. Fan, C. Wu, Y. Saleem and P. -C. Lim, “Reinforcement Learning Models and Algorithms for Diabetes Management,” in IEEE Access, vol. 11, pp. 28391-28415 (2023).
[7] A. Satwekar, T. Volpentesta, P. Spagnoletti and M. Rossi, “An Orchestration Framework for Digital Innovation: Lessons From the Healthcare Industry,” in IEEE Transactions on Engineering Management, vol. 70, no. 7, pp. 2465-2479, July (2023), doi: 10.1109/TEM.2022.3167259.
[8] J. Botes, W. Bam and I. de Kock, “Investigating the integration of public and private pharmaceutical supply chains,” 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association For Management of Technology (IAMOT) Joint Conference, Nancy, France (2022).
[9] S. Lee, J. Kim, S. W. Park, S.-M. Jin and S.-M. Park, “Toward a fully automated artificial pancreas system using a bioinspired reinforcement learning design: In silico validation”, IEEE J. Biomed. Health Informat., vol. 25, no. 2, pp. 536-546, Feb. (2021).
[10] 1. K. Agnihotri, P. Chilbule, S. Prashant, P. Jain and P. Khobragade, “Generating Image Description Using Machine Learning Algorithms,” 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India (2023).
[11] A. Jafar, A. E. Fathi and A. Haidar, “Long-term use of the hybrid artificial pancreas by adjusting carbohydrate ratios and programmed basal rate: A reinforcement learning approach”, Comput. Methods Programs Biomed., vol. 200, pp. 1-11, Mar. (2021).
[12] Z. Liu, L. Ji, X. Jiang, W. Zhao, X. Liao, T. Zhao, et al., “A deep reinforcement learning approach for type 2 diabetes mellitus treatment”, Proc. IEEE Int. Conf. Healthcare Informat. (ICHI), pp. 1-9, Nov. 2020.
[13] M. A. Raheb, V. R. Niazmand, N. Eqra and R. Vatankhah, “Subcutaneous insulin administration by deep reinforcement learning for blood glucose level control of type-2 diabetic patients”, Comput. Biol. Med., vol. 148, pp. 1-10, Sep. (2022).
[14] R. Padmanabhan, N. Meskin and W. M. Haddad, “Optimal adaptive control of drug dosing using integral reinforcement learning”, Math. Biosci., vol. 309, pp. 131-142, Mar. (2019).
