Topic outline

  • General

    Course logoDigital health is a new and fast growing field where technology meets health. In addition to use technology to enhance human health, this field tries to make medicine more personalized. In this course, we will review methods and recent advances in the field of digital health with a focus on the quantification of human behavior (with the tools of data science and machine learning). This course will give an overview of devices and sensors, methods and computational tools, and different areas where digital devices are used to enhance people’s  health and well-being. After the initial lectures by the instructor, students will pick the topic that they are mostly interested in. Each topic comes with a list of articles which can help students to understand their topics better and dig into it deeper. Each student will give an oral presentation about their chosen topic and write a final report in form of a review article at the end of the course. As part of the the assignments of the course there will be exercises to improve scientific writing skills and learning how to write a review paper.  


    Prerequisites

    This course does not have any formal requirements, however it is mostly targeted towards MSc students who have at least completed one year of their studies and PhD students. For this course some familiarity with statistical learning methods, data science, or machine learning is needed. The course will require reading, understanding and summarizing research papers for the purpose of giving presentations about them and writing a final review paper. So it is not recommended for students who do not have any previous experience with writing longer reports or summary of research outcomes. There will however be assignments in the course which will help the students to improve their scientific writing skills. If you are unsure whether you have the necessary background/skills to take this course, please contact the instructor:  talayeh.aledavood@aalto.fi

    Workload

    Attending the lectures (6 total) is compulsory (one lecture can be missed if the lecturer is previously notified and if it is not the week of your own presentation). During the first session the topics will be introduced and each student will pick a topic to work on. Each week there will be some general assignments (to be returned in the form of small reports). Each student picks one week to give a detailed presentation about their topic. As the final project of the course, each student will work on a report about their assigned topic in the format of a review paper. The length of this paper has to be minimum 2500 words (excluding the list of references). Students will also evaluate other students’ presentations and reports. 
    This course is a 5Cr course which is equivalent to 135 hours of work. Here is rough breakdown of the time that needs to be spent for different course activities:
    Attendance in lectures: 12 hours
    Preparation of presentation: 20-25 hours
    Peer evaluation and providing course feedback: 10-15 hours
    Working on the final report: 60-65 hours
    Weekly assignments (total): 20-25 hours


    Grading

    In this version of the course there is no PASS/FAIL option! A grade between 0-5 is given to the students based on the returned assignments, oral presentation(s), final report, and participation in peer-evaluation and course feedback. The breakdown of grades: weekly assignments 20% (graded by the instructor), Oral presentation 20% (graded by peers and instructor), Evaluating peers -both oral presentations and final reports (2-3 reports per student) and providing course feedback 15% (graded by the instructor), final report 45% (graded by peers and the instructor). Please note that in addition to lecture attendance, giving the oral presentation and returning the final report of minimum 2500 words are mandatory for passing the course. 



    Lecture dates and place

    Thu 18.04.2019 at 14:15 - 16:00, R030/T6 A136
    Thu 25.04.2019 at 14:15 - 16:00, R030/T6 A136
    Thu 02.05.2019 at 14:15 - 16:00, R030/T6 A136
    Thu 09.05.2019 at 14:15 - 16:00, R030/T6 A136
    Thu 16.05.2019 at 14:15 - 16:00, R030/T6 A136
    Thu 23.05.2019 at 14:15 - 16:00, R030/T6 A136