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
He et al., "The Practical Implementation of Artificial Intelligence Technologies in Medicine," Nature Medicine, 2019.
Kelly et al., "Key Challenges for Delivering Clinical Impact with Artificial Intelligence," BMC Medicine, 2019.
Haque et al., "Illuminating the Dark Spaces of Healthcare with Ambient Intelligence," Nature, 2020.
Week 2:
Depression | Parkinson's | Multi-task Learning | Wearable | Smartphone
Sept. 19 - 23
Lu et al., "Joint Modeling of Heterogenous Sensing Data for Depression Assessment with Multi-task Learning," ACM IMWUT, 2018.
Lonini et al., "Wearable Sensors for Parkinson's Disease: Which Data are Worth Collecting for Training Symptom Detection Models," npj Digital Medicine, 2018.
Moshe et al., "Predicting Symptoms for Depression and Anxiety using Smartphone and Wearable Data," Frontier Psychiatry, 2021.
Week 3:
Diabetes | Ottis Media | Reinforcement Learning | Wearable | Smartphone
Sept. 26 - 30
Yom-Tov et al. "Encouraging Physical Activity in Patients with Diabetes: Intervention using a Reinforcement Learning System," JMIR, 2017.
Gosavi et al., "A Tutorial on Reinforcement Learning," Missouri University of Science and Technology, 2019.
Bartolome et al., "A Computational Framework for Discovering Digital Biomarkers of Glycemic Control," npj Digital Medicine, 2022.
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
Litjens et al., "A Survey on Deep Learning in Medical Image Analysis," Medical Image Analysis, 2017.
Esteva et al. "A Guide to Deep Learning in Healthcare," Nature Medicine, 2019.
Esteva et al. "Dermatologist-level Classification of Skin Cancer with Deep Neural Networks," Nature, 2017.
Lu et al., "AI-based Pathology Predicts Origins for Cancers of Unknown Primary," Nature, 2021.
Komorowski et al., "The Artificial Intelligence Clinician Learns Optimal Treatment Strategies for Sepsis in Intensive Care," Nature Medicine, 2018.
R4 due on 10/3
P2 out on 10/5
In-class activity for 10/6
Tues (10/4): Research Paper Presentations
Tate T. & Patrick N. - Paper 1
Maxwell A. - Paper 2
Thurs (10/6): Research Paper Presentations
William C. - Paper 3
Piper - Paper 4
Week 5:
Cardiac Transplant | Infectious Diseases | NLP
Oct 10 - 14
Lipkova et al., "Deep-learning enabled assessment of cardiac allograft rejection from endomyocardial biopsies," Nature Medicine, 2022.
Mishra et al., "Pre-symptomatic Detection of COVID-19 from Smartwatch Data," Nature Biomedical Engineering, 2020.
Esteva et al. "COVID-19 Information Retrieval with Deep Learning based Semantic Search, Question Answering, and Abstractive Summarization," npj Digital Medicine, 2021.
R5 due on 10/10
P2 due on 10/14
P3 out on 10/12
Tues (10/11): Research Paper Presentations
Andrea R. - Paper 2
William R. - Paper 3
Thurs (10/13): Guest Speaker
Guest: Jana Lipkova, Ph.D., Harvard University
Project Team Huddles
Week 6:
Radiology| CNN | Cardiovascular | ECG
Oct 17 - 21
Yamashita et al., "Convolutional Neural Networks: An Overview of Application in Radiology," Insights into Imaging, 2018.
Petmezas et al., "Automated Atrial Fibrillation Detection using Hybrid CNN-LSTM Network on Imbalanced ECG Datasets," Biomedical Signal Processing & Control, 2021.
Ballinger et al., "DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction," AAAI, 2018.
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 Presentation
Xiaoyu W. & Vafa B. - Paper 3
Project time (in class)
Week 7:
GANs | Transfer Learning | Cancer | Stroke
Oct 24 - 28
Luo et al. "Multi-variate Time Series Imputation with Generative Adversial Networks," NeurIPS, 2018.
Alzubaidi et al., "Novel Transfer Learning for Medical Imaging with Limited Labeled Data," Cancers, 2021.
Lee et al., "Machine Learning Approach to Identify Stroke within 4.5 hours," Stroke, 2020.
R7 due on 10/24
P3 due on 10/26
Tues (10/25): Research Paper Presentations
John B. & Tal S. - Paper 2
Project time (in class)
Thurs (10/27): Research Paper Presentations
Yash S. & Arden G - Paper 3
Week 8:
Project Time
Oct. 31 - Nov. 4
*No class* Project Time On Your Own (11/1)
Project Time - in class (11/3)
P4 out on 10/31
Week 9:
Your Research
Nov. 7 - 11
Final Presentations (11/8)
Group 5 (~10:15am)
Group 3 (~10:35am)
-- 5 mins break --
Group 7 (~11:00am)
Group 2 (~11:20am)
Final Presentations (11/10)
Group 1 (~10:15am)
Group 6 (~10:35am)
Group 4 (~10:55am)
P4 (Final Paper) due on 11/11
Project & Team Evaluation (due on 11/11)
Final Presentation Evaluation (.docx | .pdf)
Tues (11/8) & Thurs (11/10):
Final Project Presentations
Week 10:
Nov. 14 - 18
Congratulations on a great term!
No assigned papers
Extras:
Domingos, "A Few Useful Things to Know about Machine Learning," Communications of the ACM, 2012.
Gulshan et al., "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs," JAMA, 2016.
Hurley et al., "A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders," ACM HEALTH, 2020.
Bartolome et al., "GlucoMine: A Case for Improving the Use of Wearable Device Data in Diabetes Management," Proc. ACM Int. Mobile Wearable Technology, 2021.
Rose et al., "A longitudinal big data approach for precision health," Nature Medicine, 2019.
Tseng et al., "Using Behavioral Rhythms and Multi-Task Learning to Predict Fine-Grained Symptoms of Schizophrenia," Scientific Reports, 2020.
Yang et al., "Artificial Intelligence-enabled Detection and Assessment of Parkinson's Disease using Nocturnal Breathing," Nature Medicine, 2022.
Bera et al., "Artificial Intelligence in digital pathology - new tools for diagnosis and precision oncology," Nature Reviews Clinical Oncology, 2019.
Chen et al., "Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning," Cancer Cell, 2022.
Afsar et al., "From hand-crafted to deep-learning-based cancer radiomics: challenges and new opportunities," IEEE Signal Processing Magazine, 2019.
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.
Liu et al., "CA-Net: Leveraging Contextual Features for Lung Cancer Prediction," Int. Conf. on Medical Image Computing & Computer Assisted Intervention (MICCAI), 2021.
Peng et al., "Self-Paced Contrastive Learning for Semi-Supervised Medical Image Segmentation with Meta-Labels," Neural Information Processing Systems (NeurIPS), 2021.
Debener et al., "Unobtrusive Ambulatory EEG using a Smartphone and Flexible Printed Electrodes around the Ear," Scientific Reports, 2015.