Prototyping a smart medication management system with machine learning-based dosage recommendations
| dc.contributor.author | S.Kavya -1NH21EE098 Syeda Mehak Fathima- 1NH21EE118 G. Ravi Kishore -1NH21CE020 M.Gautham Reddy-1NH21CE031 | |
| dc.date.accessioned | 2025-01-20T05:23:14Z | |
| dc.date.available | 2025-01-20T05:23:14Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Medication management is a critical component of healthcare that directly influences the effectiveness of treatment and patient outcomes. Despite significant advancements in medical technology, many patients still face challenges in adhering to prescribed medication regimens. This project addresses the pressing need for an innovative solution to optimize medication management by leveraging the power of machine learning (ML). The proposed solution is the development of a Smart Medication Management System (SMMS) that uses ML algorithms to recommend personalized medication dosages based on individual patient data, such as age, weight, medical history, and other relevant health factors.The primary goal of this project is to design and prototype an intelligent system capable of providing accurate, real-time dosage recommendations tailored to each patient. The system aims to minimize the risks associated with under or overdosing, reduce medication errors, and improve patient compliance with prescribed treatment plans. By utilizing a data-driven approach, the system can adapt to changes in a patient’s health status over time, ensuring that medication regimens remain optimal and effective. Furthermore,the systemintegrates medication reminders, alerts for potential drug interactions, and feedback on adverse effects, offering a comprehensive solution for both patients and healthcare providers.Machine learning models, particularly supervised learning techniques, form the backbone of the system’s decision-making process. These models are trained on extensive datasets, including medical records and drug information, allowing the system to analyze various factors affecting medication dosages. By using historical health data and real-time inputs, the system can predict the most suitable medication dosages and automatically adjust them as needed. The continuous learning aspect of machine learning ensures that the system’s recommendations improve over time with more data, leading to better decision-making and more personalized treatment. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/17322 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | Prototyping a smart medication management system with machine learning-based dosage recommendations | |
| dc.type | Learning Object |