Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time
A formidable challenge in designing sequential treatments is to determine when and in which context it is best to deliver treatments. Consider treatment for individuals struggling with chronic health conditions. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. That is, the treatment is adapted to the individual's context; the context may include current health status, current level of social support and current level of adherence for example. Data sets on individuals with records of time-varying context and treatment delivery can be used to inform the construction of the decision rules. There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment. Here we discuss our work in designing online "bandit" learning algorithms for use in personalizing mobile health interventions.
Susan
A.
Murphy,
currently
at
the
University
of
Michigan
will
be
joining
Harvard
University
in
September
2017.
Her
research
focuses
on
improving
sequential,
individualized,
decision
making
in
health,
in
particular
on
clinical
trial
design
and
data
analysis
to
inform
the
development
of just-in-time
adaptive
interventions
in
mobile
health.
She
is a
Fellow
of
the
Institute
of
Mathematical
Statistics,
a
Fellow
of
the
College
on
Problems
in
Drug
Dependence,
a
former
editor
of
the
Annals
of
Statistics,
a
member
of
the
US
National
Academy
of
Sciences,
the
US
National
Academy
of
Medicine
and
a
2013
MacArthur
Fellow.