Traditional medical research methods cannot solve modern healthcare systems problems (see one example to support this claim). Information technologies have revolutionized many areas of research and development for instance banking and commerce. Taking into consideration these two facts, OpenSourceResearch organisation (OSRC) was created.
OSRC is an open platform to explore, develop and validate new tools to conduct medical research. These innovative tools implement information technologies in medical research through multi-disciplinary research teams approach cherished by OSRC.
The OSRC is an open-source product that encourage spin off StartUps based on these innovative research tools. In this model OSRC is an academic institute, incubator and/or accelerator of StartUps. Our members can get involved in academic research as well as entrepreneurial creativity, leadership, and problem-solving activities.
This is only one of the few advantages of being a member of OSRC, you can read more and join us by clicking this link.
OSRC research tools include: computer simulation models, artificial intelligence in abdomen radiology, big data mining, synthetic and augmented data in addition to crowd science.
We would like to keep the open-source nature of this organisation but at the same time sustain development of OSRC and advance the career of our members.
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics.
Large datasets that are diverse and representative (of the heterogeneity of phenotypes in the gender, ethnicity and geography of the individuals or patients, and in the healthcare systems, workflows and equipment used) are necessary to develop and refine best practices in evidence-based medicine involving artificial intelligence. To overcome the paucity of annotated medical data in real-world settings, synthetic data are being increasingly used. Synthetic data can be created from perturbations using accurate forward models (that is, models that simulate outcomes given specific inputs), physical simulations or AI-driven generative models. Data augmentation is another but closely related field. Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on large annotated datasets, which are expensive to be acquired, stored and processed.
Big data is data that has been defined as having all of the characteristics defined by the “4 Vs”: volume (lots of data), variety (highly diverse data), velocity (changing very fast), and veracity (hard to fully validate). Data Mining is an exploratory data-analytic process that detects interesting, novel patterns within one or more data sets (that are usually large). It employs a variety of techniques, including the machine-learning techniques and standard multivariate statistical techniques.
Electronic health records (EHRs) hold promise to improve productivity, quality, and outcomes; however, using EHRs can be cumbersome, disruptive to workflow, and off-putting to patients and clinicians. One proposed solution to this problem is the use of medical scribes to generate data for research and real-time analysis.
A growing amount of scientific research is done in an open collaborative fashion, in projects sometimes referred to as “crowd science”, “citizen science”, or “networked science”. Crowd science projects are largely characterized by two important features: participation in a project is open to a wide base of potential contributors, and intermediate inputs such as data or problem-solving algorithms are made openly available.