The Deep Digital Phenotyping Lab aims to leverage digital health technologies to identify digital biomarkers for enhanced disease monitoring.
The digitisation of healthcare is transforming the way diseases and populations are characterised and monitored. It enables more personalised healthcare to be provided by taking into account the physiological and contextual specificities of individuals. To move towards precision health, it is necessary to develop innovative approaches that encompass digital, biological, clinical and omics data. All these data would allow for more accurate characterisation of diseases and populations.
I like to work at the interface between digital epidemiology, data science and clinical research. For me the biggest challenge is now to develop innovative approaches where digital data are combined wither other omics, biological and clinical data to deeply characterise the patients.
Guy Fagherazzi, PhD, Group Leader Deep Digital Phenotyping Research Unit, Director, Department of Precision Health
digitalisation of clinical & epidemiological research
Digital cohorts: development of large, international and digital cohort studies where participants are monitored with digital solutions (smartphone app, web platform, connected devices…).
Remote patient monitoring, ePROs: monitoring of patient between clinical visits, tracking of patient reported outcomes and real-life data collection.
IT infrastructure: in-house development of secured web platforms, cloud-based data lakes and research smartphone apps for real-life health data collection.
patient & public involvement
Patient-centered research: use of digital technologies to carry out research “with” or “by” patients/study participants rather than “about” them.
Innovative methods for study participation improvement: mixed methods, qualitative approaches, recordings to improve study participation rate and minimise attrition over time.
devices, digital data & biomarkers
Voice: identification and validation of digital vocal biomarkers for enhanced remote patient monitoring. Development of novel clinical endpoints to assess treatment effectiveness in real-life.
Connected devices (Activity trackers, Continuous Glucose Monitoring devices, Pill Organisers): leveraging digital tools to collect meaningful data on lifestyle, clinical factor or key biomarkers with a limited burden for the patient/user.
Social Media: social media listening for a better understanding of a population of interest in real life (perceptions, beliefs, concerns…) or for pharmacovigilance (side effects, weak signals…)
Data-driven & AI-based approaches: combination of hypothesis-driven with data-driven analyses of cohort or clinical trial data (clustering methods, prediction)
Deep Digital Phenotyping: combination of heterogeneous sources of digital, clinical, biological, omic data to deeply characterise individuals and diseases.
Digital Twins: virtual patients/individuals with similar or close characteristics as patients seen in a consultation for whom the health status, risks of complications, and disease evolutions are known.
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