Machine Learning in Healthcare

Machine Learning in Healthcare

Savitesh Kushwaha, Rachana Srivastava, Harsh Vats, Poonam Khanna
Copyright: © 2022 |Pages: 21
DOI: 10.4018/978-1-6684-4045-2.ch003
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Abstract

Machine learning approaches are utilized in healthcare for computational decision-making in cases where critical medical data analysis is required to identify hidden linkages or anomalies that are not evident to humans. Artificial intelligence (AI) tools can assess a wide range of health data; patient data from multi-omics methods; clinical, behavioural, environmental, pharmacological data; and data from the biomedical literature to respond to research issues that necessitate a big sample size on a difficult-to-reach population. In healthcare, digitising health data has eased the development of computational models and AI systems to extract insights from the data. This chapter initially addressed the prospectus of machine learning in public health with significant focus areas. The medical devices and equipment section contain device-based modelling approaches to various diseases. The chapter also includes brief details on chatbots, wearable technologies, drug distribution systems, vending machines, and text recognition from prescriptions and medicine boxes are addressed.
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Main Focus Of The Chapter

The advancement of concurrent technologies like cloud/edge computing, mobile communication, and big data technology benefits ML models' potential for healthcare applications. ML, when combined with these technologies, can produce extremely accurate predicting outcomes and aid in developing human-centered intelligent solutions. These technologies can rejuvenate the healthcare business and enable remote healthcare services for rural and low-income areas. Healthcare providers generate diverse data and information regularly, making it challenging to assess and handle using “conventional methods.” Machine learning and deep learning approaches effectively analyze this data for meaningful insights.

Furthermore, various data sources can supplement healthcare data, including genetics, medical data, social media, and environmental data (Qayyum, Qadir, Bilal, & Al-Fuqaha, 2021). The chapter covers machine learning implementation in various healthcare domains such as public health, healthcare technologies, diseases, surveillance, chatbots and wearable technologies. Furthermore, the chapter also includes the advantages and disadvantages of machine learning in healthcare.

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