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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Zhang, Shuiting Li, Hui Jing, Qiancheng Shen, Weiyun Luo, Wei Dai, Ruping |
| 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. |
| Related Links | https://eurjmedres.biomedcentral.com/counter/pdf/10.1186/s40001-024-01764-0.pdf |
| Ending Page | 9 |
| Page Count | 9 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| DOI | 10.1186/s40001-024-01764-0 |
| Journal | European Journal of Medical Research |
| Issue Number | 1 |
| Volume Number | 29 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-03-25 |
| Access Restriction | Open |
| Subject Keyword | Medicine Public Health Infectious Diseases Internal Medicine Surgery Oncology Biomedicine Anesthesia analysis Decision-making Big data Cloud-based Platform Precision medicine Medicine/Public Health |
| Content Type | Text |
| Resource Type | Review |
| Subject | Medicine |
| Journal Impact Factor | 2.8/2023 |
| 5-Year Journal Impact Factor | 2.9/2023 |
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