261-5120-00L Machine Learning for Health Care (Spring 2019)

Semester Spring Semester 2019
Lecturers G. Rätsch
Periodicity yearly course
Language of instruction English

Abstract
The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems.

Objective
During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions.

Content
The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine:

1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc.
2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them.
3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them.
4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges.

Prerequisites / Notice
Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line

Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II.

Location

The lecture will be held at ETH in LFW C 5.

Projects

Project 1: DNA splicing prediction

Deadline: 20.03.2019

Description: See slides of exercise session 2.

Project 1 Data

 

Project 2: NLP tasks

Deadline: 10.04.2019

Project 2 description and data

 

 

Course Overview

Date Topic Course Material
21.02.2019 Lecture: Introduction Lecture Slides 01
28.02.2019 Lecture: Support Vector Machines and Kernels for Computational Biology Lecture Slides 02 (annotated)
Exercise Slides 02
Jupyter notebook
Project 1 Data
Project 1 hidden set
07.03.2019 Lecture: Natural Language Processing of Clinical Texts Lecture Slides 03
Student presentations 1. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning [Presentation]
2. deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks [Presentation]
14.03.2019 Lecture: Representational Learning Lecture Slides 04 (annotated)
Exercise Slides 04
Student presentations 1. You Are What You Tweet: Analyzing Twitter for Public Health [Presentation]
2. Ghost Cytometry [Presentation]
21.03.2019 Lecture: Introduction to Time Series Analysis Lecture Slides 05
Student presentations 1. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records [Presentation]
2. Learning Low-Dimensional Representations of Medical Concepts [Presentation]
28.03.2019 Lecture: Time Series Representations / Embeddings Lecture Slides 06
Student presentations 1. Predicting Clinical Events by Combining Static and Dynamic Information using Recurrent Neural Networks
2. Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
04.04.2019 Lecture: Survival Analysis Lecture Slides 07
Exercise Slides 07
Student presentations 1. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
2. Deep Survival Analysis
11.04.2019 Lecture: Medical Image Segmentation Lecture Slides 08
Student presentations 1. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning