Big data is commonly associated to complex analytical tasks. The complexity is prevalently due to the data context. When such contexts appear vaguely defined, it is hard to decide what analytical tools should be used. Among such associations with the notion of complexity, there are medical and biological contexts and related problems. Precision health is promising to succeed in better-translating science results to clinical practice by targeting and customizing care. Yet, it remains an abstract goal without accurately classifying diseases and defining patients. On one end, increased biomedical data volume and variety justify high hopes toward possible disruptive insights deliverable by a wealth of revealed information. On the other end, the much needed assimilation and integration of diverse health data combined with the required harmonization of heterogeneous analytical tools to analyze them, posit the necessity of developing next generation value-enabling solutions.
Clearly, both the assessment of the relevance of big data information to diagnosis and treatment, and the new modalities to convey it to the patients through efficient decision support systems remain main challenges. This Research Topic is intended to present novel technological breakthroughs and state-of-the-art developments in the big data analytics for precision health and prevention. Ultimately, more widespread applications of big data analytics in individuals’ health could lead to earlier detection of diseases, preventing them from causing real damage. Potential topics include, but are not limited to:
- Big data analytics for clinical decision support
- Natural language processing of electronic health records (EHR)
- Cancer registries and classification
- Bioinformatics for individualized health care
- Machine learning models for early disease detection
- Treatment response prediction
- New patient stratification strategies
- Integrative inference tools
- EHR phenotyping
Big data is commonly associated to complex analytical tasks. The complexity is prevalently due to the data context. When such contexts appear vaguely defined, it is hard to decide what analytical tools should be used. Among such associations with the notion of complexity, there are medical and biological contexts and related problems. Precision health is promising to succeed in better-translating science results to clinical practice by targeting and customizing care. Yet, it remains an abstract goal without accurately classifying diseases and defining patients. On one end, increased biomedical data volume and variety justify high hopes toward possible disruptive insights deliverable by a wealth of revealed information. On the other end, the much needed assimilation and integration of diverse health data combined with the required harmonization of heterogeneous analytical tools to analyze them, posit the necessity of developing next generation value-enabling solutions.
Clearly, both the assessment of the relevance of big data information to diagnosis and treatment, and the new modalities to convey it to the patients through efficient decision support systems remain main challenges. This Research Topic is intended to present novel technological breakthroughs and state-of-the-art developments in the big data analytics for precision health and prevention. Ultimately, more widespread applications of big data analytics in individuals’ health could lead to earlier detection of diseases, preventing them from causing real damage. Potential topics include, but are not limited to:
- Big data analytics for clinical decision support
- Natural language processing of electronic health records (EHR)
- Cancer registries and classification
- Bioinformatics for individualized health care
- Machine learning models for early disease detection
- Treatment response prediction
- New patient stratification strategies
- Integrative inference tools
- EHR phenotyping
