375-0003-00L Designing a Digital Biomarker (Group Project 2)
|Dozierende||F. Da Conceição Barata, F. Wortmann|
|Periodizität||jährlich wiederkehrende Veranstaltung|
|Kommentar||Nur für CAS in Digital Health|
|Kurzbeschreibung||The course gives an introduction to digital biomarkers and provides students with the foundations to develop their own digital biomarkers. More specifically, the course will cover fundamental topics such as designing observational studies, collecting, and exploring data generated by consumer-centric devices, and applying analytical methods to predict health-related outcomes.|
|Lernziel||The widespread use of mobile technologies (e.g., wearable sensors, mobile applications, social media, and location-tracking technologies) has the potential to meet the health monitoring needs of the world's aging population and the ever-growing number of chronic patients. However, this premise is based on the application of information and communication technologies that allow us to monitor patient data in many different ways. In this course we will analyze systematic ways to collect data, review the most relevant methods and applications in healthcare, discuss the main challenges they present and apply the newly gained knowledge in a project.|
The course has four core learning objectives. Students should:
• understand the anatomy of digital biomarkers
• understand the potential and applications of digital biomarkers
• be able to critically reflect and assess existing digital biomarkers
• be able to design and implement a digital biomarker
|Inhalt||The course will consist of four topic clusters that will allow the discussion of the most relevant digital|
biomarker applications in healthcare:
1) Digital Biomarkers: From biological to digital biomarkers. How are they motivated, defined and how can they be leveraged for monitoring? Prognostic vs. diagnostic vs. predictive biomarkers. Passive sensing vs. active sensing. Digital biomarker vs. Digital therapeutics.
2) Consumer-centric device data: Today, vast amount of physiological, environmental, and behavioural observations can be collected with consumer centric devices. However, deriving meaningful information from this data is difficult. We will analyze strategies for extracting knowledge from those measurements.
3) Methodology: In the last decade, neural networks (also known as “deep learning”) have helped push the boundaries of the state-of-the-art in a myriad of machine learning domains. They have also uncovered a number of different problems. We will discuss advantages and disadvantage as well as alternative methods for their application to digital biomarker data.
4) Applications: Digital biomarkers are still an emerging subfield but given that longitudinal digital biomarker data are arguably easy to acquire in large quantities, it is expected that many relevant applications will emerge in the near future. We will review and discuss current applications and challenges.
|Literatur|| Sim, Ida. "Mobile devices and health." New England Journal of Medicine 381.10 (2019): 956-968.|
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 Coravos, Andrea, Sean Khozin, and Kenneth D. Mandl. "Developing and adopting safe and effective digital biomarkers to improve patient outcomes." NPJ digital medicine 2.1 (2019): 1-5.
 van den Brink, Willem, et al. "Digital resilience biomarkers for personalized health maintenance and disease prevention." Frontiers in Digital Health 2 (2021): 54.
 Weiser, Mark. "The computer for the 21st century." ACM SIGMOBILE mobile computing and communications review 3.3 (1999): 3-11.
 Kvedar, Joseph C., et al. "Digital medicine's march on chronic disease." Nature biotechnology 34.3 (2016): 239-246.
 Meskó, Bertalan, and Marton Görög. "A short guide for medical professionals in the era of artificial intelligence." NPJ digital medicine 3.1 (2020): 1-8.
 Fogel, Alexander L., and Joseph C. Kvedar. "Artificial intelligence powers digital medicine." NPJ digital medicine 1.1 (2018): 1-4.
 Caruana, Rich, et al. "Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission." Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 2015.
 McCradden, Melissa D., et al. "Ethical limitations of algorithmic fairness solutions in health care machine learning." The Lancet Digital Health 2.5 (2020): e221-e223.
 Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.
|Voraussetzungen / Besonderes||This module is assessed based on the participant's pass/fail status of the group project (including a presentation). The project involves the development of a procedure for collecting smartwatch data and applying analytical methods to predict sleep-related outcomes. Further details will be given at the beginning of the module.|