Park Inn by Radisson Hotel London
Bath Road, Heathrow, Middlesex. UB7 0DU, London, UK
Phone : 1 (702) 988 2320
Toll Free: 1800–883-8082
Email: precision@magnusmeetings.com
September 23-25, 2019 | London, UK

Dionisio Acosta

Keynote Speaker for European Precision Medicine Conferences 2019
Dionisio Acosta
Institute of Health Informatics, University College London, UK
Title : Learning Logitudinal Phenotypes from Electronic Health Record Data : Principles and Practice

Abstract:

The clinical presentation of several important diseases, such as atrial fribrillation, stroke, cancers and dementia, vary significantly, making intrinsically difficult to identify patients at risk, to discover disease sub-phenotypes, to understand the development of co-morbidites, to select the best therapeutic actions, to optimise service delivery and allocation of healthcare resources. A precise understanding of the clinical trajectory that patients follow along their disease care pathway is the key to enable the precision medicine agenda by creating data-driven patient-specific care-pathways. In this talk we present state-of-the-art computational and machine learning approaches that further our understanding clinical disease-trajectories by supporting the construction of data-driven longitudinal disease phenotypes and demonstrate how these can be used for early diagnosis, patient-specific care plan design, prognostication and finally for automatic planning and scheduling of service delivery activities.

Audience Take Away:

  • Learn about the importance of understanding disease clinical trajectories and the definition of data-driven longitudinal phenotypes.
  • Learn of novel computational and machine learning model to the understanding of disease progression.
  • Learn how disease longitudinal phenotypes can be used for early diagnosis, treatment planning and prognostication.
  • Learn how disease clinical trajectories can be applied in planning and scheduling of clinical services, offering optimal resource utilisation and patient experience.
  •  Learn how data-driven longitudinal phenotypes can be used alongside standard omics epidemiological analysis to inform drug discovery and the conduct of clinical trials.

Biography:

Dionisio Acosta is the Director of the Graduate Programme in Health Informatics at the University College London (UCL) Institute of Health Informatics. He is an expert on the design, implementation and evaluation of clinical decision support systems using AI argumentation, statistical and machine learning approaches. He is the UCL lead in a GSK-UCL Collaboration on EHR for Drug Discovery, working on ML methods for discovering longitudinal phenotypes for drug targeting using routinely collected EHR data. He is also investigator in the British Women’s Health Heart Study and was UCL Co-investigator in the EU-IMI Project EHR4CR (http://ehr4cr.eu, http://insiteplatform.com).