In recent decades, there has been an expansion in research on traumatic brain injury (TBI). However, TBI remains a global public health concern and a leading cause of death and disability worldwide. Increasingly common in health research is the application of machine learning and data mining approaches using big data, including but not limited to health administrative data and electronic health records. These analytic approaches and data sources have the potential to support targeted prevention of TBI, facilitate early and personalized intervention, and accurate prediction of clinical and treatment outcomes. Collectively, these advances announce a new era of precision medicine that could transform current approaches to TBI.
This Research Topic will highlight innovations in data science in TBI research. We welcome research that leverages and/or links big data and applies, tests, and/or validates machine learning and other analytic approaches to prevent TBI, inform evidence-based healthcare, and advance precision medicine. This includes, but is not limited to, the following research on TBI across the lifespan:
• Primary, secondary, tertiary prevention of TBI
• Identification of TBI subphenotypes
• Prediction of treatment outcomes
• Identification of differential outcome trajectories following TBI
• Factors that affect access to and quality of healthcare, including early intervention/treatment
• Early detection of adverse outcomes post injury (e.g., health outcomes such as secondary health conditions; quality of life; community re-integration; public health burden such as increased healthcare use, etc.)
For this Research Topic, we welcome the following article types: Brief Research Reports, Methods, and Original Research. We also welcome Reviews that summarize innovations in data science and data analytics in the field of TBI. All research submitted to this Research Topic is encouraged to address sex, gender, and other identity factors.
In recent decades, there has been an expansion in research on traumatic brain injury (TBI). However, TBI remains a global public health concern and a leading cause of death and disability worldwide. Increasingly common in health research is the application of machine learning and data mining approaches using big data, including but not limited to health administrative data and electronic health records. These analytic approaches and data sources have the potential to support targeted prevention of TBI, facilitate early and personalized intervention, and accurate prediction of clinical and treatment outcomes. Collectively, these advances announce a new era of precision medicine that could transform current approaches to TBI.
This Research Topic will highlight innovations in data science in TBI research. We welcome research that leverages and/or links big data and applies, tests, and/or validates machine learning and other analytic approaches to prevent TBI, inform evidence-based healthcare, and advance precision medicine. This includes, but is not limited to, the following research on TBI across the lifespan:
• Primary, secondary, tertiary prevention of TBI
• Identification of TBI subphenotypes
• Prediction of treatment outcomes
• Identification of differential outcome trajectories following TBI
• Factors that affect access to and quality of healthcare, including early intervention/treatment
• Early detection of adverse outcomes post injury (e.g., health outcomes such as secondary health conditions; quality of life; community re-integration; public health burden such as increased healthcare use, etc.)
For this Research Topic, we welcome the following article types: Brief Research Reports, Methods, and Original Research. We also welcome Reviews that summarize innovations in data science and data analytics in the field of TBI. All research submitted to this Research Topic is encouraged to address sex, gender, and other identity factors.
