redictive models have, by their learning ability (the famous “deep learning”), the possibility of improving the distinction between these two types of factors in the future .
In addition to these debates on traditional methods of analysis, it is worth emphasizing the role that the patient may have to play in the actual collection of data. As its name suggests, “Big data” is developing massive methods of collecting patient data. In this movement, connected tools, such as portals, and in general access to the Internet, have paved the way for data collection carried out by the patients themselves. It is one of the indirect effects of digital health to favor data collection channels coming directly from the patient. This is what we call the “data-driven patient”. The benefits are potentially significant in the area of clinical research. Traditional clinical trials are based on standardized data collection protocols managed by clinical research assistants. Direct transmission by patients can represent a very significant source of savings in the course of the trial. Consequently, many initiatives seek to develop methods based on data collected by patients after obtaining their consent. Through these movements emerges an evolution of research methods towards personalization. It challenges traditional approaches. It is still difficult to understand the real meaning of this questioning: will randomized controlled trials on samples – the “gold standard” of clinical research – see their place reduced? Or will they (more likely) be supplemented by other methods managing greater variability in data?
Generally speaking, the next few years should bring about an evolution in methods and also in the fields of health research, particularly in the field of clinical research.
Today, much of this new cancer data is already publicly available in the Atlas of Genomic Cancer (TGCA). It is therefore already possible to use this data in order to develop predictive models for given situations (for example the prediction of the mutation of the KRAS gene by using gene expression in patients, the response to a type of drug, etc. .). These approaches are the subject of numerous works under the expression of medicine not personalized, but precision.
However, there are limits. It is important to understand that the manipulation of a large amount of data, if it offers advantages, does not eliminate all the problems during their treatment. For example, the “noise” linked to random phenomena does not disappear with a large number of variables. There is a need to correct unwanted latent structures (preprocessing step such as background correction and normalization). Despite their large number, it is also possible that the data remains contextual. Studies are paradoxically carried out on small samples in very local situations. The challenge is to organize analyzes on a large number of patients, most often at the international level.
In addition, the manipulation of this large number of variables constitutes a major challenge: it is necessary to avoid over-learning, over-fitting (overfitting), and manage the rate of false discoveries (FDR, False Discovery Rate), especially in oncology. due to tumor heterogeneity. For example, a test can be analytically valid without clinical utility. Finally, despite its gigantic aspect, the data analysis, the hypotheses and the associated prediction models, are based solely on the so-called coding genome, in other words on only 1% of the total genome. It is easy to understand that potentially decisive information can be omitted from the results presented. The challenge is to sequence the entire genome of a tumor and compare it with the genome of a patient without cancer. There is great hope, the cost of this research decreasing as knowledge accumulates.
• New paradigms of medical practice from digital health
One consequence of the expansion of research fields is the potential influence on medical practices.
On the one hand, the new data processing capabilities introduce pure “data analysts” into medical practice, alongside d