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Automated Machine Learning for Healthcare and Clinical Notes Analysis

College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia
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Academic Editors: Antonio Celesti, Ivanoe De Falco, Antonino Galletta and Giovanna Sannino
Computers 2021, 10(2), 24; https://doi.org/10.3390/computers10020024
Received: 1 February 2021 / Revised: 15 February 2021 / Accepted: 17 February 2021 / Published: 22 February 2021
(This article belongs to the Special Issue Artificial Intelligence for Health)
Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes. View Full-Text
Keywords: AutoML; machine learning; natural language processing; clinical coding; clinical notes AutoML; machine learning; natural language processing; clinical coding; clinical notes
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MDPI and ACS Style

Mustafa, A.; Rahimi Azghadi, M. Automated Machine Learning for Healthcare and Clinical Notes Analysis. Computers 2021, 10, 24. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020024

AMA Style

Mustafa A, Rahimi Azghadi M. Automated Machine Learning for Healthcare and Clinical Notes Analysis. Computers. 2021; 10(2):24. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020024

Chicago/Turabian Style

Mustafa, Akram; Rahimi Azghadi, Mostafa. 2021. "Automated Machine Learning for Healthcare and Clinical Notes Analysis" Computers 10, no. 2: 24. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020024

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