Anesthesia decision analysis using a cloud-based big data platform
Abstract Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | European Journal of Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40001-024-01764-0 |
| _version_ | 1797233748739096576 |
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| author | Shuiting Zhang Hui Li Qiancheng Jing Weiyun Shen Wei Luo Ruping Dai |
| author_facet | Shuiting Zhang Hui Li Qiancheng Jing Weiyun Shen Wei Luo Ruping Dai |
| author_sort | Shuiting Zhang |
| collection | DOAJ |
| description | Abstract Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications. |
| first_indexed | 2024-04-24T16:21:06Z |
| format | Article |
| id | doaj.art-68111ea30d084f26b5bc7993bc114337 |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-04-24T16:21:06Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-68111ea30d084f26b5bc7993bc1143372024-03-31T11:13:50ZengBMCEuropean Journal of Medical Research2047-783X2024-03-012911910.1186/s40001-024-01764-0Anesthesia decision analysis using a cloud-based big data platformShuiting Zhang0Hui Li1Qiancheng Jing2Weiyun Shen3Wei Luo4Ruping Dai5Department of Anesthesiology, The Second Xiangya Hospital, Central South UniversityDepartment of Anesthesiology, The Second Xiangya Hospital, Central South UniversityDepartment of Otolaryngology Head and Neck Surgery, Hengyang Medical School, The Affiliated Changsha Central Hospital, University of South ChinaDepartment of Anesthesiology, The Second Xiangya Hospital, Central South UniversityDepartment of Anesthesiology, The Second Xiangya Hospital, Central South UniversityDepartment of Anesthesiology, The Second Xiangya Hospital, Central South UniversityAbstract Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.https://doi.org/10.1186/s40001-024-01764-0Anesthesia analysisDecision-makingBig dataCloud-basedPlatformPrecision medicine |
| spellingShingle | Shuiting Zhang Hui Li Qiancheng Jing Weiyun Shen Wei Luo Ruping Dai Anesthesia decision analysis using a cloud-based big data platform European Journal of Medical Research Anesthesia analysis Decision-making Big data Cloud-based Platform Precision medicine |
| title | Anesthesia decision analysis using a cloud-based big data platform |
| title_full | Anesthesia decision analysis using a cloud-based big data platform |
| title_fullStr | Anesthesia decision analysis using a cloud-based big data platform |
| title_full_unstemmed | Anesthesia decision analysis using a cloud-based big data platform |
| title_short | Anesthesia decision analysis using a cloud-based big data platform |
| title_sort | anesthesia decision analysis using a cloud based big data platform |
| topic | Anesthesia analysis Decision-making Big data Cloud-based Platform Precision medicine |
| url | https://doi.org/10.1186/s40001-024-01764-0 |
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