Date(s) - 05/23/2017
3:30 pm - 4:30 pm


Seattle WA

This seminar will feature Gang Luo on “Automating machine learning model building with big clinical data”.

Predictive modeling is fundamental for extracting value from large clinical data sets, or “big clinical data,” advancing clinical research, and improving healthcare. Predictive modeling can facilitate appropriate and timely care by forecasting an individual’s health risk, clinical course, or outcome. Machine learning is a major approach to predictive modeling using algorithms improving automatically through experience, but two factors make its use in healthcare challenging.

First, before training a model, the user of a machine learning software tool must manually select a machine learning algorithm and set one or more model parameters termed hyper-parameters. The algorithm and hyper-parameter values used typically impact the resulting model’s accuracy by over 40%, but their selection requires special computing expertise as well as many labor-intensive manual iterations. Second, most machine learning models are complex and give no explanation of prediction results.

Nevertheless, explanation is essential for a learning healthcare system.

To automate machine learning model building with big clinical data, we are currently developing a software system that can perform the following tasks in a pipeline automatically:

(a) select effective machine learning algorithms and hyper-parameter values to build predictive models;
(b) explain prediction results to healthcare researchers;
(c) suggest tailored interventions; and
(d) estimate outcomes for various configurations, which is needed for determining a proper strategy to deploy a predictive model in a healthcare system.

This talk will present the design, initial implementation, and some preliminary results of the software system.