Slides of presentation of the team
Personalized medicine by optimal prescription of treatment
Ex of projects:
- Optimal individualized treatment regime of plasma transfusion for severe trauma (superlearner with missing values)
- Survival causal inference when combining RCT and observational data (from estimating the RMST to defining optimal policy)
- Variables selection when transporting the causal effect from an RCT to a target population
- Sensitivity analysis when identifiability assumptions are violated
- Dynamic treatment regimes with missing values
Personalized medicine by integration of different data sources
Ex of projects:
- Measures of dependencies between random vectors
- Handling (Informative) missing values in time series structured by blocks
- Predictive intervals for machine learning algorithms (with missing values)
- Federated learning: collaboration between hospitals
Beyond methodological developments, innovative responses to the public health challenge first posed by respiratory allergies are targeted. For more than 30 years, there has been an increase in a number of chronic non-communicable diseases (NCD), such as asthma and allergies, respiratory diseases. Allergies are the fourth most common chronic disease in the world. The World Health Organization (WHO) predicts that by 2050, one in two people in the world will suffer from allergies (Bousquet, 2009; inserm press). In France, the number of people suffering from allergies has doubled in 20 years, particularly among children and young people. Although the expression of these diseases results from the interaction between the genetic background and the environment, especially through epigenetic mechanisms, their sudden increase is solely due to the environmental changes that occurred in the last decades because of the Western lifestyle, the genetic heritage requiring centuries to change. Reaching a better understanding of the complexity of chronic NCD, requires analysing large heterogeneous data.
There is a gap between the numerous ML methods developed and what is currently used in practice. In a review paper on ML in the field of allergy based on a bibliometric analysis over the past 50 years, Fontanella et al. (2021) have noted that only 15% of the 3000 articles which use data analysis, resort to ML tools, that are quite simple (1.7\% use random forest and 0.9\% use deep learning tools). They also highlighted that “very few research findings have been translated into clinically actionable solutions”. The lack of confidence in these new tools may be due to a lack of interpretability (black box effect), of uncertainty quantification, of reproducibility and of practitioner’s training. The sequential work between clinicians and methodologists rather than involvement of both parties at each stage of development, is another limitation. Nevertheless, a major lock is the lack of consultation, discussion, involvement of all stakeholders and regulators.
Fontanella et al. (2021) have also called for collaborative science and great care when analysis results to avoid blindly trusting the results of the algorithms; all these components are at the core of PreMeDICaL.
Ex. of projects:
- Benralitrap project. CT air-trapping characterization for the early identification of Benralisumab responders among eosinophilic asthma patients
- CRO-AIT project: Combining Allergen ImmunoTherapy Randomized controlled trials and Observational studies (project with ALK)