Automated Machine Learning for Healthcare and Clinical Notes Analysis
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 provi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/10/2/24 |
| _version_ | 1797395675181219840 |
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| author | Akram Mustafa Mostafa Rahimi Azghadi |
| author_facet | Akram Mustafa Mostafa Rahimi Azghadi |
| author_sort | Akram Mustafa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:37:54Z |
| format | Article |
| id | doaj.art-e6ed4782409647129b7b47c875765c4a |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T00:37:54Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-e6ed4782409647129b7b47c875765c4a2023-12-11T18:01:59ZengMDPI AGComputers2073-431X2021-02-011022410.3390/computers10020024Automated Machine Learning for Healthcare and Clinical Notes AnalysisAkram Mustafa0Mostafa Rahimi Azghadi1College of Science and Engineering, James Cook University, Townsville, QLD 4811, AustraliaCollege of Science and Engineering, James Cook University, Townsville, QLD 4811, AustraliaMachine 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.https://www.mdpi.com/2073-431X/10/2/24AutoMLmachine learningnatural language processingclinical codingclinical notes |
| spellingShingle | Akram Mustafa Mostafa Rahimi Azghadi Automated Machine Learning for Healthcare and Clinical Notes Analysis Computers AutoML machine learning natural language processing clinical coding clinical notes |
| title | Automated Machine Learning for Healthcare and Clinical Notes Analysis |
| title_full | Automated Machine Learning for Healthcare and Clinical Notes Analysis |
| title_fullStr | Automated Machine Learning for Healthcare and Clinical Notes Analysis |
| title_full_unstemmed | Automated Machine Learning for Healthcare and Clinical Notes Analysis |
| title_short | Automated Machine Learning for Healthcare and Clinical Notes Analysis |
| title_sort | automated machine learning for healthcare and clinical notes analysis |
| topic | AutoML machine learning natural language processing clinical coding clinical notes |
| url | https://www.mdpi.com/2073-431X/10/2/24 |
| work_keys_str_mv | AT akrammustafa automatedmachinelearningforhealthcareandclinicalnotesanalysis AT mostafarahimiazghadi automatedmachinelearningforhealthcareandclinicalnotesanalysis |