Abstract
BackgroundNowadays, trendy research in biomedical sciences juxtaposes the term ‘precision’ to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform these large datasets into knowledge. Despite such progress, many barriers still exist against achieving precision medicine and precision public health interventions for the benefit of the individual and the population.Main bodyThe present work focuses on analyzing both the technical and societal hurdles related to the development of prediction models of health risks, diagnoses and outcomes from integrated biomedical databases. Methodological challenges that need to be addressed include improving semantics of study designs: medical record data are inherently biased, and even the most advanced deep learning’s denoising autoencoders cannot overcome the bias if not handled a priori by design. Societal challenges to face include evaluation of ethically actionable risk factors at the individual and population level; for instance, usage of gender, race, or ethnicity as risk modifiers, not as biological variables, could be replaced by modifiable environmental proxies such as lifestyle and dietary habits, household income, or access to educational resources.ConclusionsData science for precision medicine and public health warrants an informatics-oriented formalization of the study design and interoperability throughout all levels of the knowledge inference process, from the research semantics, to model development, and ultimately to implementation.
Highlights
Nowadays, trendy research in biomedical sciences juxtaposes the term ‘precision’ to medicine and public health with companion words like big data, data science, and deep learning
The National Institutes of Health (NIH) defines precision medicine as the “approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person” [3]
In this work, we have discussed the promises of precision medicine and precision public health, as well as the challenges we face to leverage big data for precision care that could lead to effective advancements and translational implementations
Summary
We have discussed the promises of precision medicine and precision public health, as well as the challenges we face to leverage big data for precision care that could lead to effective advancements and translational implementations. We have revisited some of the definitions and described a hybrid theory-based and data-driven approach that can aid with the processes of study design and model inference. The top-down approach relies on strong prior knowledge, which can be used to guide study design (e.g. domain selection, observational units, cohort identification) and test specific hypotheses (such as in clinical trials). Precision medicine demands interdisciplinary expertise that understands and bridges multiple disciplines and domains up to a point where the fulcrum of the research is located on the bridges themselves. This defines transdisciplinarity, knowledge discovery going beyond disciplines, which demands new research and development paradigms
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