A Tour of Survival Analysis, from Classical to Modern
Abstract: Survival analysis is used for predicting time-to-event outcomes, such as how long a patient will stay in the hospital, or when the recurrence of a tumor will likely happen. This tutorial aims to go over the basics of survival analysis, how it is used in healthcare, and some of its recent methodological advances from the ML community. We will also discuss open challenges. NOTE: This tutorial has a corresponding notebook: https://sites.google.com/view/chil-survival.
Population and public health: challenges and opportunities
Abstract: In this tutorial, we will describe population and public health and their essential role in a comprehensive strategy to improve health. We will illustrate state of the art data and modeling approaches in population and public health. In doing so, we will identify overlaps with and open questions relevant to machine learning, causal inference and fairness.
Public Health Datasets for Deep Learning: Challenges and Opportunities
Abstract: With today's publicly available, de-identified clinical datasets, it is possible to ask questions like, “Can an algorithm read an electrocardiogram as well as a cardiologist can?” However, other kinds of questions like, “Does this ECG relate to a later cardiac arrest?” can’t be answered with the limited public data available to us today. Research using private datasets gives us reason to be optimistic, but progress will be slow unless suitable de-identified datasets become open, allowing researchers to efficiently collaborate and compete. Learn about an effort underway at the University of Chicago, led by Ziad Obermeyer, Sendhil Mullainathan, and their team, to provide a secure and public “ImageNet for clinical data” that balances the concerns of patients, healthcare institutions, and researchers.
State of the Art Deep Learning in Medical Imaging
Abstract: This tutorial will be styled as a graduate lecture about medical imaging with deep learning. This will cover the background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks. These methods will be covered in terms of architecture and objective function design. Also, a discussion about incorrect feature attribution and approaches to mitigate the issue. Prerequisites: basic knowledge of computer vision (CNNs) and machine learning (regression, gradient descent).
Analyzing critical care data, from speculation to publication, starring MIMIC-IV (Part 1)
Abstract: Despite a wealth of data, only a small fraction of decisions in critical care are evidence based. In this tutorial we will start with the conception of an idea, solidify the hypothesis, operationalize the concepts involved, and execute the study in a reproducible and communicable fashion. We will run our study on MIMIC-IV, an update to MIMIC-III, and cover some of the exciting additions in the new database. This tutorial will be interactive and result in a study performed end-to-end in a Jupyter notebook. Technical expertise is not required, as we will form groups based on skill level.
Analyzing critical care data, from speculation to publication, starring MIMIC-IV (Part 2)
Abstract: Despite a wealth of data, only a small fraction of decisions in critical care are evidence based. In this tutorial we will start with the conception of an idea, solidify the hypothesis, operationalize the concepts involved, and execute the study in a reproducible and communicable fashion. We will run our study on MIMIC-IV, an update to MIMIC-III, and cover some of the exciting additions in the new database. This tutorial will be interactive and result in a study performed end-to-end in a Jupyter notebook. Technical expertise is not required, as we will form groups based on skill level.