[cs-talks] Friday Nov 13: Data Seminar 1:00pm MCS 148

Natali Ruchansky natalir at bu.edu
Thu Nov 12 21:59:45 EST 2015

Friday November 13, 1:00pm MCS138

*Speaker*: Jen Gong (MIT http://web.mit.edu/jengong/www/)
*Title*: Instance-weighting for patient-specific risk stratification models
Accurate risk models for adverse outcomes can provide important input to
clinical decision-making. Surprisingly, one of the main challenges when
using machine learning to build clinically useful risk models is the small
amount of data available. Risk models need to be developed for specific
patient populations, specific institutions, specific procedures, and
specific outcomes. With each exclusion criterion, the amount of relevant
training data decreases, until there is often an insufficient amount to
learn an accurate model. This difficulty is compounded by the large class
imbalance that is often present in medical applications.

We present an approach to address the problem of small data using transfer
learning methods in the context of developing risk models for cardiac
surgeries. We explore ways to build surgery-specific and hospital-specific
models (the *target task*) using information from other kinds of surgeries
and other hospitals (*source tasks*). We propose a novel method to weight
examples based on their similarity to the target task training examples to
take advantage of the useful examples while discounting less relevant ones.

We show that incorporating appropriate source data in training can lead to
improved performance over using only target task training data, and that
our method of instance weighting can lead to further improvements. Applied
to a surgical risk stratification task, our method, which used data from
two institutions, performed comparably to the risk model published by the
Society for Thoracic Surgeons, which was developed and tested on over one
hundred thousand surgeries from hundreds of institutions.
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