Big Data in Medicine, the Present and Hopefully the Future

Abstract

The emergence of data coming from different venues, as several “omic” approaches, is providing already compelling evidence that the smart use of this information could provide invaluable information to prevent, diagnose and treat human diseases. However, the most daunting challenges remain ahead, as the explosive accumulation of data from additional perspectives, including social graphs, biosensors, and imaging, promise to deliver crucial information that could be exploited for the improvement of the entire human race, both in developed, and developing countries, optimizing health expenses and reaching also the less fortunate sections of the societies. And yet, formidable challenges remain, that pertain for the most part to the collection of the data, their organization, and most relevantly their integration. Here we provide few, pointed examples to the present relevance of these big data approaches in human health as well potential road maps toward the implementation of broader data collections and analyses.

The use of forest plot to identify article similarity and differences in characteristics between journals using medical subject headings terms

“…Much more knowledge in publications is required for explorations [ 1 ] for readers who are interested in understanding data accumulating at a high velocity, resulting in datasets too large for traditional data-processing systems. [ 2 , 3 ] In practice, data in health care need to incorporate increasingly sophisticated analytical and visual approaches in ways making the data meaningful and useful to humans in our lives.…”

Isabl Platform, a digital biobank for processing multimodal patient data

“…By assigning users to groups, the Isabl administrator can manage the actions granted towards different resources. Isabl offers 3 main roles: (1) Managers are users who can register samples, (2) analysts can run analyses, and (3) engineers can do both, register samples and run analyses. These roles are optional and customizable.…”

“…With an ever expanding suite of high-throughput technologies [ 1 ], the pace at which the scientific community is generating data at scale has rapidly accelerated. This imposes demands for specialized expertise to support data processing and analysis [ 2 ]. Importantly, the derivation of novel biological and clinical insights is increasingly reliant upon large and statistically powered datasets, rich metadata annotation (clinical, demographic, treatment, outcome) as well as integration of diverse data modalities generated across samples and patients (i.e.…”

“…We have developed Isabl, a plug-and-play platform for the processing of individualcentric multimodal data. Isabl is designed to support: (1) management of data assets according to the FAIR [13] principles (Findable, Interoperable, Accessible, Reusable), (2) automated deployment of data processing applications following the DATA [7] reproducibility checklist (Documentation, Automation, Traceability, and Autonomy); and, (3) advanced integrations with institutional information systems across diverse data types (i.e. clinical and biospecimen databases).…”

Recursos para la evaluación de la política pública en materia de cáncer de pulmón en México

“…Por lo anterior, contar con evidencias que contribuyan a la evaluación de instrumentos regulatorios y al diseño de estrategias para hacer un uso eficiente de los recursos disponibles, es una tarea continua relevante, así mismo la aplicación de métodos analíticos que consideran grandes volúmenes de datos es un abordaje que se reconoce como cada vez más útil ( 3 – 7 ).…”