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Emerging Role of Machine Learning in Field of Pharmacology | IEEE Conference Publication | IEEE Xplore

Emerging Role of Machine Learning in Field of Pharmacology


Abstract:

The medical profession currently faces a number of difficult problems, such as rising medicine and therapy costs, and society requires particular major reforms in this fi...Show More

Abstract:

The medical profession currently faces a number of difficult problems, such as rising medicine and therapy costs, and society requires particular major reforms in this field. Pharmaceutical goods may now be produced with personalized pharmaceuticals that have the needed dosage, release characteristics, and other components based on the needs of each patient. The application of machine learning (ML) has grown across many societal sectors, especially the pharmacology field. This paper focuses on the application of ML in a number of pharmacology-related domains, including drug research and development, pharmacy efficiency improvement, and clinical trials. These programs swiftly accomplish objectives by minimizing the role of people. In addition, we discuss existing issues and prospective approaches, several ML algorithms and approaches, and future applications of ML in the healthcare sector.
Date of Conference: 23-24 December 2023
Date Added to IEEE Xplore: 27 February 2024
ISBN Information:
Conference Location: Banur, India

I. Introduction

ML-based algorithms have the potential to significantly advance research and development (R&D) through the comprehension of target-disease relationships, choosing of therapeutic applicants, protein arrangement forecasts, the generation and optimisation of molecule-level substances, the comprehension of disease procedures, the development of novel suggestive and forecasting indicators, and the examination of fingerprints information collected by mobile devices. Due to a greater dependence on digital technology for data collecting and site monitoring, the COVID-19 pandemic's effects on scientific experiment execution might hasten the adoption of ML in A medical experiment success. [1].

References

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