Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks
The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2019-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020007 |
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| author | Ying Yu Min Li Liangliang Liu Yaohang Li Jianxin Wang |
| author_facet | Ying Yu Min Li Liangliang Liu Yaohang Li Jianxin Wang |
| author_sort | Ying Yu |
| collection | DOAJ |
| description | The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine. |
| format | Article |
| id | doaj-art-2f3c544da3604fd097fad7c2e0f9769f |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-2f3c544da3604fd097fad7c2e0f9769f2025-02-02T23:47:57ZengTsinghua University PressBig Data Mining and Analytics2096-06542019-12-012428830510.26599/BDMA.2019.9020007Clinical Big Data and Deep Learning: Applications, Challenges, and Future OutlooksYing Yu0Min Li1Liangliang Liu2Yaohang Li3Jianxin Wang4<institution content-type="dept">School of Computer Science and Engineering</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country>, and the <institution content-type="dept">School of Computer Science and Technology</institution>, <institution>University of South China</institution>, <city>Hengyang </city><postal-code>421001</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Engineering</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Engineering</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Old Dominion University</institution>, <city>Norfolk</city>, <state>VA</state> <postal-code>23529</postal-code>, <country>USA</country>.<institution content-type="dept">School of Computer Science and Engineering</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country>.The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine.https://www.sciopen.com/article/10.26599/BDMA.2019.9020007deep learningclinical dataelectronic health record (ehr)medical imageclinical note |
| spellingShingle | Ying Yu Min Li Liangliang Liu Yaohang Li Jianxin Wang Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks Big Data Mining and Analytics deep learning clinical data electronic health record (ehr) medical image clinical note |
| title | Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks |
| title_full | Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks |
| title_fullStr | Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks |
| title_full_unstemmed | Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks |
| title_short | Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks |
| title_sort | clinical big data and deep learning applications challenges and future outlooks |
| topic | deep learning clinical data electronic health record (ehr) medical image clinical note |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020007 |
| work_keys_str_mv | AT yingyu clinicalbigdataanddeeplearningapplicationschallengesandfutureoutlooks AT minli clinicalbigdataanddeeplearningapplicationschallengesandfutureoutlooks AT liangliangliu clinicalbigdataanddeeplearningapplicationschallengesandfutureoutlooks AT yaohangli clinicalbigdataanddeeplearningapplicationschallengesandfutureoutlooks AT jianxinwang clinicalbigdataanddeeplearningapplicationschallengesandfutureoutlooks |