Machine Learning for Health


Jump to Feb. or Mar.

Date Subject Readings Student Presentation Deadlines
Jan 10, 2019 Lecture 1: Why is healthcare unique?
Jan 17, 2019 Lecture 2: Supervised Learning for Classification, Risk Scores and Survival
  • Grey Kuling on Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Reflection Questions on Required Papers:
  • Contrast the predictive tasks (e.g., labels in the objective function) in each of the required papers; what are the benefits and drawbacks of each?
  • Would you deploy any of these supervised models?
Jan 24, 2019 Lecture 3: Causal inference with observational data
  1. David Burns on Deep Barcodes for Fast Retrieval of Histopathology Scans
  2. Adamo Young and Michael Dimmick on GRAM: Graph-based Attention Model for Healthcare Representation Learning
  3. Geoff Klein and Matt Hemsley on Deep MR to CT Synthesis using Unpaired Data
  4. Phil Boyer on What-If Reasoning with Counterfactual Gaussian Processes
Office Hours for Homework will be held on Wednesday, Jan 23 at 4-6pm in GB 405.

Reflection Questions on Required Papers:
  • Describe causal identifiability, and its impact on learning with observational data?
  • What is the difference between a standard Gaussian Process (GP) and a causal GP (CGP) objective function? How does that impact learning?
Jan 31, 2019 Lecture 4: Fairness, Ethics, and Healthcare
  1. Matthew MacKay and Amanjit Singh Kainth on Learning Fair Representations
  2. Duc Truong on Equality of Opportunity in Supervised Learning
  3. Punit Shah on Evaluating Reinforcement Learning Algorithms in Observational Health Settings
  4. Devin Singh on Use of GANs in Medical Imaging
Reflection Questions on Required Papers:
  • What are ways that bias can enter into a machine learning model's predictions?
  • Are there reasons that bias in medical data may be harder to detect or distentangle?
Feb 7, 2019 Lecture 5: Clinical Time Series Modelling
  1. Allen Lee
  2. Kelvin Wong and Shun Da Suo on Disease-Atlas: Navigating Disease Trajectories using Deep Learning
  3. Seung Eun Yi and Chantal Shaib on Clinical Intervention Prediction and Understanding with Deep Neural Networks
  4. Pouria Mashouri on Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings
Homework 1 due at 11:59 PM on MarkUs
The code for extracting the data from the MIMIC psql database is here
Feb 14, 2019 Lecture 6: Clinical Imaging (Radiology/Pathology)
  1. Sapir Labes on Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
  2. Hong Yue Sean Liu and Yan Li on Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
  3. Srinivasan Sivanandan on CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
  4. Vineeth Bhaskara Dermatologist-level classification of skin cancer with deep neural networks
Project proposals due at 5PM here.

Reflection Questions on Required Papers:
  • What are some cirtical differences between clinical imaging and images found in standard vision datasets?
  • Are there standard pre-processing or modelling techniques that don't make sense in clinical images?
Feb 21, 2019 Lecture 7: Clinical NLP and Audio
  1. Sumeet Ranka and Vaibhav Saxena on Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
  2. Yingying Fu and Eric Wan on Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
  3. Jienan Yao and Matthew Wong on Natural Language Processing, Electronic Health Records, and Clinical Research
Reflection Questions on Required Papers:
  • How could external knowledge could be added to clinical NLP models and for what tasks might that make sense?
  • How could clinical NLP benefit from using structured EHR data?
Feb 28, 2019 Lecture 8: Clinical Reinforcement Learning
  1. Sindhu C M Gowda on A reduced dimension fMRI shared response model
  2. Daniel Dastoor and Joanna Pineda on Scalable and accurate deep learning with electronic health records
  3. Brenna Li on Meaningless comparisons lead to false optimism in medical machine learning
  4. Sneha Desai on A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
Reflection Questions on Required Papers:
  • What are particular problems with off-policy evaluation in a clinical RL setting?
  • What problems can arise with the action and state spaces chosen in the papers?
Mar 7, 2019 Lecture 9: Missingness and Representations
  1. Jacob Kelly and Jeevaa Velayutham on Semi-supervised Biomedical Translation with Cycle Wasserstein Regression GANs
  2. Pulkit Mathur and Zhen Gou on Recommender Systems: Missing Data and Statistical Model Estimation
  3. Yuyang Liu and Chris Meaney on Recurrent Neural Networks for Multivariate Time Series with Missing Values
  4. Angeline Yasodhara and Marta Skreta on Why is my Classifier Discriminatory?
Reflection Questions on Required Papers:
  • Briefly discuss the techniques used in these papers to address the missingness/sparsity in electronic medical records.
  • How does the current patient state affect the types of data that are collected in the future?
  • Discuss if/how the models capture and propogate the patients' previous states for predicting clinical events.
Mar 14, 2019 Lecture 10: Generalization and transfer learning
  1. Alex Lu and Amy Lu on Implications of non-stationarity on predictive modeling using EHRs
  2. Fizza Ahmad Sheikh and Daniel Hidru on Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
  3. Sean Segal and Sergio Casas on Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests
  4. Zhen Yang and Chenzi Qie on Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing
Reflection Questions on Required Papers:
  • Does multi-task learning inherently create more generalizable models?
  • Are there conditions for which non-stationarity could be ignored in model training and deployment?
Mar 21, 2019 Lecture 11: Interpretability / Humans-In-The-Loop / Policies and Politics
  1. Pouria Mashouri on Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings
  2. Siva Manivasagam and Min Bai on Rationalizing Neural Predictions
  3. Shagun Gupta and Chun-Hao Chang on Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
  4. Angad Kalra on Why policymakers should care about “big data” in healthcare
Reflection Questions on Required Papers:
  • What are possible ways that a model could be verified as interpretable?
  • Should machine learning models deployed in healthcare settings be held to a higher standard than other application areas?
  • What concerns should policy makers have about deploying models to minimize hospital costs?
Mar 28, 2019 Course Presentations
April 4, 2019 Course Presentations
April 11, 2019 Projects Due Project report due 11:59PM