STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Open Source Developments Review Article

Exploring the Development of AI Models Using Open-Source Tools to Predict Patient Outcomes and Optimize Treatment Plans

  1. Ushaa Eswaran Department of Electronics and Communication Engineering, Mahalakshmi Tech Campus Affiliated to Anna University, Chennai

Abstract

Integrating artificial intelligence (AI) into healthcare offers a transformative opportunity to enhance patient care and clinical decision-making. Through the use of predictive analytics, AI can significantly enhance the accuracy of outcome predictions and assist in developing personalized treatment plans that cater to each patient’s specific needs. This paper delves into the development of AI models using open-source tools, which are increasingly favored for their accessibility, collaborative nature, and capacity for rapid innovation. Open-source frameworks, such as TensorFlow and PyTorch, empower healthcare professionals and researchers to develop sophisticated machine learning algorithms without the constraints of proprietary software. We examine various methodologies employed in the creation of these models, including data preprocessing, feature selection, and algorithm training, highlighting best practices for maximizing accuracy and effectiveness. Additionally, the paper presents several compelling case studies that demonstrate successful applications of AI in predicting critical health outcomes, such as readmission rates and disease progression. Despite the promising potential of AI, challenges remain in implementing these technologies within clinical settings, including issues related to data privacy, integration with existing systems, and the need for ongoing validation of models. This paper aims to provide insights into both the benefits and obstacles of adopting AI in healthcare, ultimately underscoring the importance of open-source tools in facilitating the future of patient-centered care. Through a detailed exploration of current advancements, this research contributes to the growing body of knowledge on how AI can be harnessed to improve healthcare delivery.

Keywords

References (18)

  1. Syed FM, Mulla F, Kousar E. AI in securing electronic health records (EHR) systems. Int J Adv Eng Technol Innov. 2024;1(2):593–620.
  2. Ali NA. Predictive Analytics for the Modern Enterprise. California, United States: O’Reilly Media; 2024.
  3. Diaz O, Kushibar K, Osuala R, Linardos A, Garrucho L, Igual L, et al. Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. Physica Medica. 2021;83:25-37. doi:10.1016/j.ejmp.2021.02.007
  4. Santosh K, Gaur L. Artificial Intelligence and Machine Learning in Public Healthcare. SpringerBriefs in Applied Sciences and Technology. 2021. doi:10.1007/978-981-16-6768-8
  5. Planche B, Andres E. Hands-On Computer Vision with Tensorflow 2: Leverage Deep Learning to Create Powerful Image Processing Apps with Tensorflow 2.0 and Keras. Birmingham, United Kingdom: Packt Publishing Ltd.; 2019.
  6. Ansel J, Yang E, He H, Gimelshein N, Jain A, Voznesensky M, et al. PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2. 2024:929-947. doi:10.1145/3620665.3640366
  7. Boudali I, Chebaane S, Zitouni Y. A predictive approach for myocardial infarction risk assessment using machine learning and big clinical data. Healthcare Analytics. 2024;5:100319. doi:10.1016/j.health.2024.100319
  8. Gharibi G, et al. Automated end-to-end management of the modeling lifecycle in deep learning. Empir Softw Eng. 2021;26:1–33.
  9. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. The American Journal of Medicine. 2019;132(7):795-801. doi:10.1016/j.amjmed.2019.01.017
  10. Mahdi MM, Al Rizq MO, Alhammam MY, Al-Baibaa MH, Al Saleem MH, Al-Baibaa HH, Al Khatrah AMJ. Critical analysis of sociological factors impacting healthcare delivery in exploring social determinants of health and strategies for addressing inequities. Chelonian Res Found. 2022;17(2):918–29.
  11. Negro-Calduch E, Azzopardi-Muscat N, Krishnamurthy RS, Novillo-Ortiz D. Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews. International Journal of Medical Informatics. 2021;152:104507. doi:10.1016/j.ijmedinf.2021.104507
  12. Kühnel L, Schneider J, Perrar I, Adams T, Moazemi S, Prasser F, et al. Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis results. Scientific Reports. 2024;14(1). doi:10.1038/s41598-024-62102-2
  13. Basireddy MR. Implement AI technique in Python to help healthcare professionals in early detection, treatment planning, and patient monitoring. Journal of Artificial Intelligence, Machine Learning and Data Science. 2023;1(4):607-611. doi:10.51219/jaimld/maheswara-reddy-basireddy/157
  14. Duan Y, Edwards JS, Dwivedi YK. Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management. 2019;48:63-71. doi:10.1016/j.ijinfomgt.2019.01.021
  15. Moor M, Rieck B, Horn M, Jutzeler CR, Borgwardt K. Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review. Frontiers in Medicine. 2021;8. doi:10.3389/fmed.2021.607952
  16. Jamei M, Nisnevich A, Wetchler E, Sudat S, Liu E. Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PLoS One. 2017;12:e0181173. doi:10.1371/journal.
  17. Hossain ME. Predictive modelling of the comorbidity of chronic diseases: A network and machine learning approach. [Thesis]. University of Sydney; 2020.
  18. Graeßner M, Jungwirth B, Frank E, Schaller SJ, Kochs E, Ulm K, et al. Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data. Scientific Reports. 2023;13(1). doi:10.1038/s41598-023-33981-8
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