Schedule

Reading List

1. Yang et al., "Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes," CHI, 2019.

2. Beam et al., "Challenges to the Reproducibility of Machine Learning Models in Healthcare," JAMA Network, 2020.

1. Radin et al., "The Hopes and Hazards of using Personal Health Technologies in the Diagnosis and Prognosis of Infections," The Lancet Digital Health, 2021.

2. He et al., "The Practical Implementation of Artificial Intelligence Technologies in Medicine," Nature Medicine, 2019.

3. Vollmer et al., "Machine Learning and Artificial Intelligence Research for Patient Benefit: 20 Critical Questions on Transparency, Replicabililty, Ethics, and Effectiveness," BMJ, 2020.

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

1. Lonini et al., "Wearable Sensors for Parkinson's Disease: Which Data are Worth Collecting for Training Symptom Detection Models," npj Digital Medicine, 2018.

2. Cohen et al., "Detecting Rare Diseases in Electronic Health Records using Machine Learning and Knowledge Engineering: Case Study of Acute Hepatic Porphyria," PLoS One, 2020.*

3. Lampos et al., "Tracking COVID-19 using Online Search," npj Digital Medicine, 2021.

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

1. Mortazavi et al., "Analysis of Machine Learning Techniques for Heart Failure Readmission," Circulation: Cardiovascular Quality & Outcomes, 2016.

2. Sangha et al., "Automated Multilabel Diagnosis on Electrocardiographic Images and Signals," Nature Communications, 2022.

3. Bowman et al., "A Method for Characterizing Daily Physiology from Widely Used Wearables," Cell Report Methods, 2021.

4. Walch et al., "A Global Quantification of "Normal" Sleep Schedules using Smartphone Data," Science Advances, 2016.

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

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

3. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks", Nature, 2017.

4. Huang et al., "Predicting Lung Cancer Risk at Follow-up Screening with Low-Dose CT: A Training and Validation Study of a Deep Learning Method," The Lancet Digital Health, 2019.

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

1. Barnett et al., "Relapse Prediction in Schizophrenia through Digital Phenotyping: A Pilot Study," Neuropsychopharmacology, 2018.

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

3. 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.

4. Bone et al., "Dynamic Prediction of Psychological Treatment Outcomes: Development and Validation of a Prediction Model using Routinely Collected Symptom Data," Lancet Digital Health, 2021.

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

2. Gu et al., "SugarMate: Non-intrusive Blood Glucose Monitoring with Smartphones," IMWUT, 2017.

3. Bora et al., "Predicting the Risk of Developing Diabetic Retinopathy using Deep Learning," Lancet Digital Health, 2021.

4. Nandakumar et al., "Opiod Overdose Detection using Smartphones," Science Translational Medicine, 2019.

5. Chan et al., "Contactless Cardiac Arrest Detection using Smart Devices," npj Digital Medicine, 2019.

No assigned papers | Project Time (in class)

Happy Summer & Congratulations to all that are graduating!

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

2. Litjens et al., "A Survey on Deep Learning in Medical Image Analysis," Medical Image Analysis, 2017.

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

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

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

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

7. Bent et al., "Engineering Digital Biomarkers of Interstitial Glucose from Noninvasive Smartwatches," npj Digital Medicine, 2021.

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

9. 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.

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

11. Severson et al., "Discovery of Parkinson's Disease States and Disease Progression Modelling: A Longitudinal Data Study using Machine Learning," The Lancet Digital Health, 2021.

12. Bharat et al., "Big Data and Predictive Modeling for the Opioid Crisis: Existing Research and Future Potential," Lancet Digital Health, 2021.

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