Filipe Da Conceição Barata: Catalogue data in Autumn Semester 2022

Name Dr. Filipe Da Conceição Barata
Address
Professur Informationsmanagement
ETH Zürich, WEV G 214
Weinbergstr. 56/58
8092 Zürich
SWITZERLAND
Telephone+41 44 632 35 09
E-mailfbarata@ethz.ch
URLhttp://n.ethz.ch/~dfilipe
DepartmentManagement, Technology, and Economics
RelationshipLecturer

NumberTitleECTSHoursLecturers
351-0778-00LDiscovering Management
Entry level course in management for BSc, MSc and PHD students at all levels not belonging to D-MTEC. This course can be complemented with Discovering Management (Excercises) 351-0778-01.
3 credits3GB. Clarysse, S. Brusoni, F. Da Conceição Barata, H. Franke, V. Hoffmann, P. Tinguely, L. P. T. Vandeweghe
AbstractDiscovering Management offers an introduction to the field of business management and entrepreneurship for engineers and natural scientists. By taking this course, students will enhance their understanding of management principles and the tasks that entrepreneurs and managers deal with. The course consists of theory and practice sessions, presented by a set of area specialists at D-MTEC.
ObjectiveThe general objective of Discovering Management is to introduce students into the field of business management and entrepreneurship.

In particular, the aims of the course are to:
(1) broaden understanding of management principles and frameworks
(2) advance insights into the sources of corporate and entrepreneurial success
(3) develop skills to apply this knowledge to real-life managerial problems

The course will help students to successfully take on managerial and entrepreneurial responsibilities in their careers and / or appreciate the challenges that entrepreneurs and managers deal with.
ContentThe course consists of a set of theory and practice sessions, which will be taught on a weekly basis. The course will cover business management knowledge in corporate as well as entrepreneurial contexts.

The course consists of three blocks of theory and practice sessions: Discovering Strategic Management, Discovering Innovation Management, and Discovering HR and Operations Management. Each block consists of two or three theory sessions, followed by one practice session where you will apply the theory to a case.

The theory sessions will follow a "lecture-style" approach and be presented by an area specialist within D-MTEC. Practical examples and case studies will bring the theoretical content to life. The practice sessions will introduce you to some real-life examples of managerial or entrepreneurial challenges. During the practice sessions, we will discuss these challenges in depth and guide your thinking through team coaching.

Through small group work, you will develop analyses of each of the cases. Each group will also submit a "pitch" with a clear recommendation for one of the selected cases. The theory sessions will be assessed via a multiple choice exam.
Lecture notesAll course materials (readings, slides, videos, and worksheets) will be made available to inscribed course participants through Moodle. These course materials will form the point of departure for the lectures, class discussions and team work.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Social CompetenciesCommunicationassessed
Self-presentation and Social Influence assessed
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
363-1163-00LDeveloping Digital Biomarkers Restricted registration - show details
Particularly suitable for students with a technical background who are interested in healthcare.
3 credits2VF. Da Conceição Barata
AbstractThe 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.
ObjectiveThe 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 Machine Learning algorithms that allow us to use this 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 practical assignments.

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
ContentThe 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 behavioral observations can be collected with consumer centric devices. To derive clinical meaningful information from this data is, however, 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 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 in digital biomarker data are arguably easy to acquire in large quantities, it is expected that many relevant Machine Learning applications will emerge in the near future. We will review and discuss current applications and challenges.
Literature[1] Sim, Ida. "Mobile devices and health." New England Journal of Medicine 381.10 (2019): 956-968.
[2] Schatz, Bruce R. "Population measurement for health systems." NPJ Digital Medicine 1.1 (2018): 1-4.
[3] 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.
[4] van den Brink, Willem, et al. "Digital resilience biomarkers for personalized health maintenance and disease prevention." Frontiers in Digital Health 2 (2021): 54.
[5] Weiser, Mark. "The computer for the 21st century." ACM SIGMOBILE mobile computing and communications review 3.3 (1999): 3-11.
[6] Kvedar, Joseph C., et al. "Digital medicine's march on chronic disease." Nature biotechnology 34.3 (2016): 239-246.
[7] 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.
[8] Fogel, Alexander L., and Joseph C. Kvedar. "Artificial intelligence powers digital medicine." NPJ digital medicine 1.1 (2018): 1-4.
[9] 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.
[10] 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.
[11] Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.
Prerequisites / NoticeSome programming experience in Python is required, and some experience in Machine Learning is highly recommended.
375-0003-00LDesigning a Digital Biomarker (Group Project 2) Restricted registration - show details
Only for CAS in Digital Health
4 credits1GF. Da Conceição Barata, F. B. Wortmann
AbstractThe 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.
ObjectiveThe 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
ContentThe 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.
Literature[1] Sim, Ida. "Mobile devices and health." New England Journal of Medicine 381.10 (2019): 956-968.
[2] Schatz, Bruce R. "Population measurement for health systems." NPJ Digital Medicine 1.1 (2018): 1-4.
[3] 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.
[4] van den Brink, Willem, et al. "Digital resilience biomarkers for personalized health maintenance and disease prevention." Frontiers in Digital Health 2 (2021): 54.
[5] Weiser, Mark. "The computer for the 21st century." ACM SIGMOBILE mobile computing and communications review 3.3 (1999): 3-11.
[6] Kvedar, Joseph C., et al. "Digital medicine's march on chronic disease." Nature biotechnology 34.3 (2016): 239-246.
[7] 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.
[8] Fogel, Alexander L., and Joseph C. Kvedar. "Artificial intelligence powers digital medicine." NPJ digital medicine 1.1 (2018): 1-4.
[9] 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.
[10] 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.
[11] Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.
Prerequisites / NoticeThis 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.