AI-Based Decision Support System for Traumatic Brain Injury: A Survey.
Flora RajaeiShuyang ChengCraig A WilliamsonEmily WittrupKayvan NajarianPublished in: Diagnostics (Basel, Switzerland) (2023)
Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.
Keyphrases
- traumatic brain injury
- artificial intelligence
- big data
- deep learning
- electronic health record
- machine learning
- severe traumatic brain injury
- decision making
- risk factors
- cardiovascular disease
- cardiovascular events
- multiple sclerosis
- data analysis
- type diabetes
- air pollution
- single cell
- quantum dots
- sensitive detection
- mild traumatic brain injury