Machine Learning in Healthcare System

Abstract

Machine learning (ML) has materialized as a valuable tool in the healthcare sector, empowering medical professionals to provide better patient care and manage clinical data efficiently. As an application of artificial intelligence (AI), ML algorithms analyze vast datasets, recognize patterns, and autonomously execute tasks without explicit instructions. This technology finds diverse applications in healthcare, such as collecting and organizing patient data, identifying healthcare trends, and suggesting personalized treatments. Recognizing its potential to enhance decision-making and reduce risks, hospitals and healthcare companies have embraced ML, leading to exciting career opportunities in the field. The accessibility of commanding hardware and cloud computing has further facilitated the widespread embracing of ML across various domains, including healthcare. With the copious amount of patient data generated, ML's integration in healthcare holds immense promise, enabling successful applications in the industry. ML algorithms in healthcare play a crucial role in transforming the way medical data is processed, analyzed and utilized to improve patient outcomes. These algorithms leverage large datasets and employ various techniques to extract meaningful patterns and insights. Some of the key areas where ML is applied in healthcare are as follows: electronic health record (EHR) analysis, drug discovery and development, personalized medicine, healthcare resource management, disease progression modeling, remote patient monitoring and predictive analytics for healthcare operations

Cite this paper

@misc{juliet2024machine,
  title = {{Machine Learning in Healthcare System}},
  author = {Juliet, A. Hency and Sathya, K.},
  pages = {153--178},
  year = {2024},
  doi = {10.1002/9781394242528.ch7}
}

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