A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions.
BACKGROUND
Data-driven risk stratification models built using data from a single hospital often have a paucity of training data. However, leveraging data from other hospitals can be challenging owing to institutional differences with patients and with data coding and capture.


OBJECTIVE
To investigate three approaches to learning hospital-specific predictions about the risk of hospital-associated infection with Clostridium difficile, and perform a comparative analysis of the value of different ways of using external data to enhance hospital-specific predictions.


METHODS
We evaluated each approach on 132 853 admissions from three hospitals, varying in size and location. The first approach was a single-task approach, in which only training data from the target hospital (ie, the hospital for which the model was intended) were used. The second used only data from the other two hospitals. The third approach jointly incorporated data from all hospitals while seeking a solution in the target space.


RESULTS
The relative performance of the three different approaches was found to be sensitive to the hospital selected as the target. However, incorporating data from all hospitals consistently had the highest performance.


CONCLUSIONS
The results characterize the challenges and opportunities that come with (1) using data or models from collections of hospitals without adapting them to the site at which the model will be used, and (2) using only local data to build models for small institutions or rare events.


CONCLUSIONS
We show how external data from other hospitals can be successfully and efficiently incorporated into hospital-specific models.
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Jenna
J
Wiens
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
John
J
Guttag
Microsoft Research, Redmond, Washington, USA.
Eric
E
Horvitz
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D016360:Clostridioides difficile; D003015:Clostridium Infections; D003428:Cross Infection; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*; D014481:United States; D055815:Young Adult
c. difficile; electronic health records; predictive models; risk stratification; transfer learning