Optimal Treatment of Depression Case Study – Georgia Tech – Health Informatics in the Cloud


Earlier we talked about the potential
applications of analytics to the determination
of optimal treatment. That often now means the best
results at the lowest costs. These are of course not precisely
defined terms and there is serious debate about issues such as the value of
spending substantial sums on treatments that only prolong the life of terminal
patients by small increments in time. The research shown here
illustrates the potential to help with less controversial
clinical contexts. A partially observable Markov decision
process, a variation that introduces memory, was developed and compared
to the actual treatment of nearly 6,000 depressed patients, abstracted
from their electronic records. I leave it to you to read this section
in the text for the details, but as shown here, under certain assumptions
the model delivered improvements that were better than or
almost as good as real physicians, but at a better cost per
unit of improvement. As we seek to re-engineer the health
care system, technologies like this may become a routine part of
clinical decision support. Some would even speculate that, at least in certain circumstances,
they may replace physicians. But I leave it to you to consider that.

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