Big data hurdles in precision medicine and precision public health

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.

“We’ve got the home care data, what do we do with it?”: understanding data use in decision making and quality improvement

“…While some participants discussed specific barriers, such as connectivity issues in rural areas, the most common challenge was organizations feeling like they were insufficiently equipped to make sense of the data, to make it meaningful and useful for their organization. This is consistent with Prosperi and colleagues' assertion that an abundance of data does not necessarily translate to the ability to use those data for targeted improvements in health services delivery [17]. The data must be matched with commensurate resources, training and personnel.…”

The role of a virtual avatar in attention and memory tasks in Rett Syndrome

“…We consider the findings reported in this article a relevant contribution to the introduction of a new generation of embodied interactive virtual agents (interactive avatars) aimed at supporting intellectually disabled patients in their everyday activities [49,50]. These findings can help families and educators to identify what sort of software can be effectively used to help patients with RTT, and also software designers to make good evidence-based choices to offer more focused software, capable of giving significant performance improvements [51,52].…”

The Role of a Virtual Avatar in Attention and Memory Tasks in Rett Syndrome

“…We consider the ndings reported in this article a relevant contribution to the introduction of a new generation of embodied interactive virtual agents (interactive avatars) aimed at supporting subjects with RTT in their everyday activities 49,50 . These ndings can help families and educators to identify what sort of software can be effectively used to help subjects with RTT, and also software designers to make good evidence-based choices to offer more focused software, capable of giving signi cant performance improvements [51][52][53][54][55][56][57][58] .…”