A Framework for Personalized Drug Prescriptions Decision Support System using Hybrid Techniques

Abstract

Abstract The increased complexities in healthcare data require the need to have intelligent systems that can be used to provide accurate as well as a personalized prescription of drugs. This research proposes a novel framework for a Personalized Drug Prescription Decision Support System (PDSS) based on improved approaches that will combine the Viterbi algorithm, neural networks, and Beam Search. The framework will take advantage of the Viterbi algorithm's modeling strength of sequence and follow the most likely course of treatment for a patient's history. As such, to overcome these limitations inherent to a Viterbi algorithm, such as local optimality and a high requirement of memory consumption, a neural network layer will be integrated into dynamically estimated transition and emission probabilities, boosting generalization and the ability to deal with various patient profiles. Additionally, Beam search will be employed to cut the computational overhead and enable exploration of multiple high-probability treatment paths, improving both efficiency and decision robustness. The proposed improved model will use 70% of the data to train and the remaining 30% to test, utilizing the Saliva-Derived SNP Datasets. The main performance indicators will be related to prescription accuracy, the time taken to make a computation, and memory efficiency. A comparison will be made and an analysis performed between the performance of the standard Viterbi algorithm as compared to the enhanced Viterbi algorithm. Early findings will verify the hypothesis that an improved system will be more successful than a traditional single-model system with its ability to apply more precise and resource-saving drug recommendations in accordance with the profiles of individual patients. This framework will present an appealing progress in clinical decision support that advocates a safer and more fruitful delivery of personalized drug prescription.