Schedule

This is a tentative schedule and is subject to change as necessary. To gain institutional access to weekly research papers, please ensure that you are logged into through the Dartmouth VPN.

Many class periods will include presentation and discussion of two seminal research papers on various Data Science for Health topics from the reading list below. Guidelines for the research paper presentations can be found on the here.

If you find that on a given week, you don't find any of the assigned papers interesting, you can grab a different paper from the "Extras" list at the bottom of this page. You are also invited and encouraged to recommend other good and transformative papers in literature that are fitting for the reading list.

Week

Reading List

Agenda

Week 1:

Overview

Sept. 12 - 16

Tues (9/13): Intro (slides)

Thurs (9/15): Exploratory Data Analysis + Project Groups (slides)

*No R1 due (research summary) for this week*

A1 out on 9/15

Week 2:

Depression | Parkinson's | Multi-task Learning | Wearable | Smartphone

Sept. 19 - 23

R2 due on 9/19

P1 out on 9/21

A1 due on 9/22

Tues (9/20): W2 slides

Research Paper Presentations

  • Sunishka J. - Paper 2

  • Julian W. - Paper 3

Thurs (9/22): Research Paper Presentations

  • Timothy Y. - Paper 1

Week 3:

Diabetes | Ottis Media | Reinforcement Learning | Wearable | Smartphone

Sept. 26 - 30

  1. Yom-Tov et al. "Encouraging Physical Activity in Patients with Diabetes: Intervention using a Reinforcement Learning System," JMIR, 2017.

  2. Bartolome et al., "A Computational Framework for Discovering Digital Biomarkers of Glycemic Control," npj Digital Medicine, 2022.

  3. Chan et al., "Detecting Middle Ear Fluid using Smartphones," Science Translational Medicine, 2019.

R3 due on 9/26

P1 due on 9/30

Tues (9/27): Research Paper Presentations

  • Hanna W. - Paper 1

  • Xingjian D. - Paper 2

Thurs (9/29): Research Paper Presentations

  • Avani K. & Gokul S. - Paper 3

Week 4:

Cancer | Sepsis | Deep Learning | Image Data

Oct 3 - 7

R4 due on 10/3

Tues (10/4): Research Paper Presentations

  • Tate T. & Patrick N. - Paper 1

  • Maxwell A. - Paper 2

Thurs (10/6): Research Paper Presentations

  • William C. & Shitong C. - Paper 3

  • Piper - Paper 4

Week 5:

Cardiac Transplant | Infectious Diseases | NLP

Oct 10 - 14

R5 due on 10/10

Tues (10/11): Guest Speaker

Thurs (10/13): Research Paper Presentations

  • Andrea R. & Vafa B. - Paper 2

  • William R. - Paper 2

Week 6:

Radiology| CNN | Cardiovascular | ECG

Oct 17 - 21

R6 due on 10/17

Tues (10/18): Research Paper Presentations

  • Scott D. & Bo Q. - Paper 1

  • Neel G. & Daniel S. - Paper 2

Thurs (10/20): Research Paper Presentations

  • TBD

Week 7:

GANs | Transfer Learning | Cancer | Stroke

Oct 24 - 28

R7 due on 10/24

Tues (10/25): Research Paper Presentations

  • John B. & Tal S. - Paper 2

  • Yash S. & Arden G - Paper 3

Thurs (10/27): Research Paper Presentations

  • TBD

Week 8:

Project Time

Oct. 31 - Nov. 4

No assigned papers | Project Time (in class)

Thurs (11/3): Project Time & Revise Final Paper

Week 9:

Your Research

Nov. 7 - 11

Final Presentations (11/8)

Final Presentations (11/10)

Tues (11/8) & Thurs (11/10):

Final Project Presentations

Week 10:

Nov. 14 - 18

Congratulations on a great term!

No assigned papers

Extras:


  1. Domingos, "A Few Useful Things to Know about Machine Learning," Communications of the ACM, 2012.

  2. Gulshan et al., "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs," JAMA, 2016.

  3. Hurley et al., "A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders," ACM HEALTH, 2020.

  4. Bartolome et al., "GlucoMine: A Case for Improving the Use of Wearable Device Data in Diabetes Management," Proc. ACM Int. Mobile Wearable Technology, 2021.

  5. Rose et al., "A longitudinal big data approach for precision health," Nature Medicine, 2019.

  6. Tseng et al., "Using Behavioral Rhythms and Multi-Task Learning to Predict Fine-Grained Symptoms of Schizophrenia," Scientific Reports, 2020.

  7. Yang et al., "Artificial Intelligence-enabled Detection and Assessment of Parkinson's Disease using Nocturnal Breathing," Nature Medicine, 2022.

  8. Bera et al., "Artificial Intelligence in digital pathology - new tools for diagnosis and precision oncology," Nature Reviews Clinical Oncology, 2019.

  9. Chen et al., "Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning," Cancer Cell, 2022.

  10. Afsar et al., "From hand-crafted to deep-learning-based cancer radiomics: challenges and new opportunities," IEEE Signal Processing Magazine, 2019.

  11. Gao et al. "Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective," Int. Conf. on Medical Image Computing & Computer Assisted Intervention (MICCAI), 2021.

  12. Liu et al., "CA-Net: Leveraging Contextual Features for Lung Cancer Prediction," Int. Conf. on Medical Image Computing & Computer Assisted Intervention (MICCAI), 2021.

  13. Peng et al., "Self-Paced Contrastive Learning for Semi-Supervised Medical Image Segmentation with Meta-Labels," Neural Information Processing Systems (NeurIPS), 2021.

  14. Debener et al., "Unobtrusive Ambulatory EEG using a Smartphone and Flexible Printed Electrodes around the Ear," Scientific Reports, 2015.