Pitfalls of Health Data Science
- The increased adoption of electronic health records (EHR) has created novel opportunities for researchers, including clinicians and data scientists, to access large, enriched patient databases. With these data, researchers are in a position to approach questions with statistical power previously unheard of in medical research. In this workshop, we present and discuss challenges in the use of EHR data for research, as well as explore the unique opportunities provided by these data.
Machine Learning for Patient Stratification and Classification
- The workshop will introduce the audience to machine learning techniques. Participants will explore the differences between supervised and unsupervised learning and practice implementing them using Python and the SciPy ecosystem. It will focus on patient stratification and classification, for decision support in the intensive care unit.
Applied Statistical Learning in Python
- This workshop aims to introduce clinicians to popular statistical methods used in machine learning, without delving into the underlying mathematical theory. We will focus on the random forest and support vector machine for classification, as well as general concepts of model fit and cross-validation. In the hands-on exercise, you will be asked to implement and evaluate these models on a clinical prediction problem. Basic understanding of the Python language and Jupyter notebook environment (as covered in Day 1 program) will be assumed.
Data for Improvement
- Data is a critical element of improvement. This workshop will provide an introduction to improvement science in healthcare. By using case studies and team-based activities, participants will learn practical skills and techniques for quality improvement – systems thinking, measurement, analyzing variation, and the model for improvement. While this session is focused on healthcare, participants will gain the ability to formulate and create changes that can have a lasting impact in other fields and disciplines. No programming skills are required.
Natural Language Processing of Health Data
- Processing huge amount of text data and turning it into actionable insights is a herculean task. Natural Language Processing (NLP) is one of the field of Artificial Intelligence that automates the process of understanding content and context within a text. By dissecting text data, NLP can help build systems that can interact with data in natural language. This workshop will discuss the basics of NLP and how it can be effectively used to automate, summarize, understand, and pull insights from health data.