Workshop on Assessing Cardiovascular Risk in a Synthetic Patient Cohort
David Dorr and Ted Laderas
This repo consists of a workshop to teach students about using clinical and genetic covariates to predict cardiovascular risk in a realistic (but synthetic) dataset.
The workshop has two parts:
- Night 1 - Exploratory Data Analysis (this repo)
- Night 2 - Machine Learning of CVD Risk
Night 1 Program:
Task 1. Asking the Question – Background of the problem, Intro to clinical data (20 min)
Learning Objective: Understand the potential risk factors of cardiovascular disease and its impact on healthcare. Format: Short Lecture (15 minutes) + Questions
Task 2. Simple CVD Risk Score Calculations (10 min)
Learning objective: To perform a simple assessment of risk factors associated with the outcomes. Format: Brief demonstration/slides (10 mins), then work time. Output: calculation of risk scores for various cohorts of interest.
Task 3. Exploring the Data and Selecting a Cohort (60 min)
Learning Objective: Given the data set, what are the problems with the current dataset? Using EDA tools to understand issues with the clinical variables and come up with solutions to clean it. Format: Short Lecture (10 minutes) + Interactive Workshop. Output: Selection of Clinical Cohort for later analysis.
Task 4. Discussion so far (30 mins)
Learning Objective: Discussion of Clinical Data and Cardiovascular risk. How well did we do? How can we do better?
Licensing and Attribution
This workshop was produced with support from NIH's Big Data to Knowledge (BD2K) Initiative at OHSU (BD2K T25 Grant: 1R25EB020379-01).
Workshop Materials are Licensed under a Creative Commons 4.0 Non Commercial License
Code is licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.