1 PhD Student, Luddy School of Informatics, Computing and Engineering, IU Indianapolis, USA.
2 Public Health Researcher, New York Medical College, New York, USA.
3 Department of Nanoscience and Nano-Engineering, University of North Carolina Greensboro, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 001-025
Received on 22 October 2024; revised on 30 November 2024; accepted on 02 December 2024
The integration of artificial intelligence [AI] into clinical trials has revolutionized the process of drug development and personalized medicine. Among these advancements, deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-time monitoring. This study explores the application of deep learning techniques, such as convolutional neural networks [CNNs] and transformer-based models, to stratify patients, forecast adverse events, and personalize treatment plans. Furthermore, predictive modelling approaches, including survival analysis and time-series forecasting, are employed to predict trial outcomes, enhancing efficiency and reducing trial failure rates. To address challenges in analysing unstructured clinical data, such as patient notes and trial protocols, natural language processing [NLP] techniques are utilized for extracting actionable insights. A custom dataset comprising structured patient demographics, genomic data, and unstructured text is curated for training and validating these models. Key metrics, including precision, recall, and F1 scores, are used to evaluate model performance, while trade-offs between accuracy and computational efficiency are examined to identify the optimal model for clinical deployment. This research underscores the potential of AI-driven methods to streamline clinical trial workflows, improve patient-centric outcomes, and reduce costs associated with trial inefficiencies. The findings provide a robust framework for integrating predictive analytics into precision medicine, paving the way for more adaptive and efficient clinical trials. By bridging the gap between technological innovation and real-world applications, this study contributes to advancing the role of AI in healthcare, particularly in fostering personalized care and improving overall trial success rates.
Deep Learning: Predictive Modelling: Clinical Trials; Personalized Medicine; Natural Language Processing; Patient Stratification
Preview Article PDF
Sydney Anuyah, Mallika K Singh and Hope Nyavor. Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care. World Journal of Advanced Research and Reviews, 2024, 24(3), 001-025. Article DOI: https://doi.org/10.30574/wjarr.2024.24.3.3671