Reading Schedule

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

Week 1 (9/15 & 9/17): A View of the Landscape

1. Koymemir et al., "Wearable and Implantable Sensors for Biomedical Applications" Annual Reviews on Analytical Chemistry, 2018.

2. Harari et al. "Using Smartphones to Collect Behavioral Data for Psychological Science: Opportunities, Practical Considerations, and Challenges" Assoc. of Psychological Science, 2016.

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

4. Esteva et al., "A Guide to Deep Learning in Healthcare," Nature Medicine, 2019.

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

Deliverables

1. Due on Thursday, 9/17 @ 9am EST

1. Summary of 1 research paper. See guidelines on the assignments page.

2. Assignment 0 (Presentation Schedule)

Week 2 (9/22 & 9/24): Infectious Diseases

1. Tuesday (9/22): Invited Speaker - Alejandro Martinez (previous DS4H student)

   • Martinez, "Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning," To be submitted, 2020.

2. Readings for the week:

   • Latif et al., "Leveraging Data Science to Combat COVID-19: A Comprehensive Review," Techxriv, 2020.

   • Ozturk et al., "Automated Detection of COVID-19 cases using Deep Neural Networks with X-ray Images," Computers in Biology and Medicine, 2020.

   • Wood et al., "Taking Connected Mobile-Health Diagnostics of Infectious Diseases to the Field," Nature, 2019. (Presenter (9/24): Anmol Chachra, live)

   • Hossain et al., "FluSense: A Contactless Syndromic Surveillance Platform for Influenza-Like Illness in Hospital Waiting Areas," Proc. ACM Interact. Mob. Wearable Ubiquitous. Technol., 2020. (Presenter (9/24): Grad TA - Abigail Bartolome, live)

   • Li et al., "Digital Health: Tracking Physiomes and Activity using Wearable Biosensors Reveals Useful Health-Related Information," PLoS Biology, 2017.

Deliverables

1. Due on Monday, 9/21 @ 11:59PM EST

   • Summary of 2 research papers from this week's reading list. See guidelines on the assignment page.

2. Due on Tuesday, 9/22 @ 10:00AM EST

   • Complete Final Project Sign-Up Form. Link is posted on Canvas.

3. Due on Thursday, 9/24 @ 11:59PM EST

   • Assignment 1 (Exploratory Data Analysis). Details on Canvas.

Week 3 (9/29 & 10/1): Cancer | Radiology

1. Yom-Tov, Screening for Cancer Using a Learning Internet Advertising System, ACM Trans. on Computing for Healthcare, 2020. (Presenter 9/29: Arij Elfaki, live)

2. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks", Nature, 2017. (Presenter 9/29: Matt Kenney, live)

3. Yamashita et al., "Convolutional Neural Networks: An Overview and Application in Radiology," Insights into Imaging, 2018. (Presenter 10/1: Bruce Zou, live)

4. Jerez et al., "Missing Data Imputation using Statistical and Machine Learning Methods in a Real Breast Cancer Problem," Artificial Intelligence in Medicine, 2010. (Presenter 10/1: Osman Khan, live)

5. Abreu et al., "Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review", ACM Computing Surveys, 2016.

Deliverables

1. Due on Monday, 9/28 @ 11:59PM EST

   • Summary of 2 research papers from this week's reading list. See guidelines on the assignment page.

   • Define Final Project Challenge/Task (must be approved by instructor)

2. Due on Thursday, 10/1 @ 11:59PM EST

   • Complete Exploratory Data Analysis & Data Description Section (first draft) for Final Paper.

Week 4 (10/6 & 10/8): Cognitive Impairment | Speech Impairments

1. Orimaye et al., "Predicting Probable Alzheimer's Disease using Linguistic Deficits and Biomarkers, BMC Bioinformatics, 2017. (Presenter 10/6: Danielle Fang, live)

2. Ladouce et al., "Understanding Minds in Real-World Environments: Toward a Mobile Cognition Approach," Front. Hum. Neuroscience, 2017. (Presenter 10/8: Abubakar Kasule, live)

3. So et al., "Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques," Applied Sciences, 2017. (Presenter 10/8: Dongkai Chen, live)

4. Rusz et al., "Smartphone Allows Capture of Speech Abnormalities Associated with High Risk of Developing Parkinson's Disease," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 2018.

Deliverables

1. Due on Monday, 10/5 @ 11:59PM EST

   • Summary of 2 research papers from this week's reading list. See guidelines on the assignment page.

2. Due on Thursday, 10/8 @ 11:59PM EST

   • Complete Introduction Section & Related Work (first draft) for Final Paper.

   • Project Page (details on Canvas)

Week 5 (10/13 & 10/15): Mental Illness | Substance Use Disorders

1. Barnett et al., "Relapse Prediction in Schizophrenia through Digital Phenotyping: A Pilot Study," Neuropsychopharmacology, 2018. (Presenter 10/13: Shreyas Agnihotri, live)

2. DeepMood: "Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks," Int. Conf. on World Wide Web, 2017. (Presenter 10/13: Chongyang Gao, live)

3. Costafreda et al., "Pattern of Neural Response to Verbal Fluency shows Diagnostic Specificity for Schizophrenia and Bipolar Disorder," BMC Psychiatry, 2011. (Presenter 10/15: Soo Park, live)

4. Nandakumar et al., "Opioid Overdose Detection using Smartphones," Science Translational Medicine, 2019. (Presenter 10/15: Dylan Wang, live)

5. Takizawa et al., "Neuroimaging-aided Differential Diagnosis of the Depressive State," NeuroImage, 2014.

Deliverables

1. Due on Monday, 10/12 @ 11:59PM EST

   • Summary of 2 research papers from this week's reading list. See guidelines on the assignment page.

2. Milestone for Thursday, 10/15 (no submission required)

2. • Preliminary idea of Method/Approach for Final Project challenge

Week 6 (10/20 & 10/22): Eye Disorders | Ear Disease

1. Schmidt-Erfurth et al., "Artificial Intelligence in Retina," Progress in Retina and Eye Research, 2018. (Presenter 10/20: Lessley Hernandez, live)

2. Fauw et al., "Clinically applicable deep learning for diagnosis and referral in retinal disease," Nature Medicine, 2018.

3. Kim et al., "Development of Machine Learning Models for Diagnosis of Glaucoma," PLOS ONE, 2017. (Presenter 10/22: Joseph Notis, live)

4. Chan et al., "Detecting Middle Ear Fluid using Smartphones," Science Translational Medicine, 2019. (Presenter 10/22: Ayan Agarwal, live)

5. Srinivasan et al., "Fully Automated Detection of Diabetic Macular Edema and Dry Age-Related Macular Degeneration from Optical Coherence Tomography Images," Biomed. Optics Express, 2014.

Deliverables

1. Due on Monday, 10/19 @ 11:59PM EST

   • Summary of 2 research papers from this week's reading list. See guidelines on the assignment page.

2. Due on Thursday, 10/22 @ 11:59PM EST

   • Complete Methods Section (first draft) for Final Paper.

Week 7 (10/27 & 10/29): Diabetes | Brain Science

1. Ahmedt-Aristizabal et al., "Deep Facial Analysis: A New Phase 1 Epilepsy Evaluation using Computer Vision," Epilepsy & Behavior, 2018. (Presenter 10/27: Pengze Liu, live)

2. Gulshan et al., "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs," JAMA, 2016. (Presenter 10/27: Yijing Feng, live)

3. Wang et al., "Smartphone-Based Wound Assessment System for Patients with Diabetes," IEEE Transactions on Biomedical Engineering, 2015. (Presenter 10/19: Ari Chadda, live)

4. Avram et al., "A Digital Biomarker of Diabetes from Smartphone-based Vascular Signals," Nature Medicine, 2020.

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

Deliverables

1. Due on Monday, 10/26 @ 11:59PM EST

   • Summary of 2 research papers from this week's reading list. See guidelines on the assignment page.

2. Due on Tuesday, 10/27 @ 11:59pm EST

   • Thinking Outside the Box assignment (see canvas for more details)

3. Due on Thursday, 10/29 @ 11:59PM EST

2. • Complete Result Section (first draft) for Final Paper.

Week 8 (11/3 & 11/5): Invited Speaker | Your Own Research Paper

1. Tuesday (11/3) - No Class; Project Time | Go and Vote

   • Incorporate all feedback received for final project

   • Revisit and revise all sections of final paper

2. Thursday (11/5): Invited Speaker - Nicholas Jacobson; Class time: 10:30 - 11:45am

   • Jacobson and Chung, "Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones," Sensors, 2020.

   • Wilhelm et al., "Cognitive-Behavioral Therapy in the Digital Age: Presidential Address," Behavior Therapy, 2020.

Week 9 (11/10 & 11/12): Final Presentations

Project Presentations (see guidelines and schedule on the Final Project page)

Deliverables

1. Submit Final Presentation Slides on Canvas (due on the presentation date)

2. Submit Final Paper (due on Friday, 11/13 @ 11:59 PM EST).

Week 10 (11/17): Class Wrap-UP

Other Papers

1. Calvo et al., "Natural Language Processing in Mental Health Applications using Non-Clinical Texts," Natural Language Engineering, 2017.

2. Che et al., "Recurrent Neural Networks for Multivariate Time Series with Missing Values," Scientific Reports, 2018.

3. Valle et al., "An Inflammatory Cytokine Signature Predicts COVID-19 Severity and Survival," Nature Medicine, 2020.

4. Nimri et al., "Insulin Dose Optimization using an Automated Artificial Intelligence-based Decision Support System in Youths with Type 1 Diabetes," Nature Medicine, 2020.

5. Avram et al., "A Digital Biomarker of Diabetes from Smartphone-based Vascular Signals," Nature Medicine, 2020.

6. Head et al., "The Extent and Consequences of P-Hacking in Science," PLOS Biology, 2015.

7. Hicks et al., "Best Practices for Analyzing Large Scale Health Data from Wearables and Smartphone Apps," npj Digital Medicine, 2019