Using Data Mining to Explore Complex Clinical Decisions: A Study of Hospitalization After a Suicide Attempt.
J Clin Psychiatry 2006;67(7):1124-1132
© Copyright 2016 Physicians Postgraduate Press, Inc.
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Background: Medical education is
moving toward developing guidelines using the
evidence-based approach; however, controlled data
are missing for answering complex treatment decisions such as those made during suicide
attempts. A new set of statistical techniques called
data mining (or machine learning) is being used by different industries to explore complex
databases and can be used to explore large clinical
Method: The study goal was to
reanalyze, using data mining techniques, a published
study of which variables predicted psychiatrists'
decisions to hospitalize in 509 suicide attempters
over the age of 18 years who were assessed in the emergency department. Patients were
recruited for the study between 1996 and 1998.
Traditional multivariate statistics were compared with
data mining techniques to determine variables
Results: Five analyses done by
psychiatric researchers using traditional statistical
techniques classified 72% to 88% of patients correctly.
The model developed by researchers with no
psychiatric knowledge and employing data mining
techniques used 5 variables (drug consumption
during the attempt, relief that the attempt was not
effective, lack of family support, being a
housewife, and family history of suicide attempts) and
classified 99% of patients correctly (99%
sensitivity and 100% specificity).
Conclusions: This reanalysis of a
published study fundamentally tries to make the point
that these new multivariate techniques, called
data mining, can be used to study large clinical
databases in psychiatry. Data mining techniques
may be used to explore important treatment
questions and outcomes in large clinical databases and
to help develop guidelines for problems where controlled data are difficult to obtain. New
opportunities for good clinical research may be
developed by using data mining analyses.