The K-nearest neighbor algorithm predicted rehabilitation potential better than current Clinical Assessment Protocol.
OBJECTIVE
There may be great potential for using computer-modeling techniques and machine-learning algorithms in clinical decision making, if these can be shown to produce results superior to clinical protocols currently in use. We aim to explore the potential to use an automatic, data-driven, machine-learning algorithm in clinical decision making.


METHODS
Using a database containing comprehensive health assessment information (the interRAI-HC) on home care clients (N=24,724) from eight community-care regions in Ontario, Canada, we compare the performance of the K-nearest neighbor (KNN) algorithm and a Clinical Assessment Protocol (the "ADLCAP") currently used to predict rehabilitation potential. For our purposes, we define a patient as having rehabilitation potential if the patient had functional improvement or remained at home over a follow-up period of approximately 1 year.


RESULTS
The KNN algorithm has a lower false positive rate in all but one of the eight regions in the sample, and lower false negative rates in all regions. Compared using likelihood ratio statistics, KNN is uniformly more informative than the ADLCAP.


CONCLUSIONS
This article illustrates the potential for a machine-learning algorithm to enhance clinical decision making.
Zhu
Mu
M
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
Chen
Wenhong
W
Hirdes
John P
JP
Stolee
Paul
P
D000203:Activities of Daily Living; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D003951:Diagnostic Errors; D006305:Health Status Indicators; D006699:Home Care Services / Q000458:organization & administration; D006801:Humans; D009864:Ontario; D018579:Patient Selection; D011379:Prognosis; D012046:Rehabilitation / Q000458:organization & administration*