Minimizing search time for finding an effective treatment: Learning a near-optimal policy using constrained algorithms, approximations, and causal inference
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Håkansson, Samuel
Lindblom, Viktor
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Patients sometimes have to try several treatments before the one that best alleviates
their symptoms is found. Since each trial of an unsuccessful treatment can be both
costly and prolong patient suffering, making this search as efficient as possible is
of great importance. We have developed a solution in two parts. (i) A constraint
that balances the need to find a better treatment versus the desire to minimize the
number of treatments tried. (ii) A dynamic programming algorithm and a greedy
algorithm that uses the constraint for finding a policy that finds a good treatment in
as few trials as possible. We also develop different methods of estimating potential
outcomes and computing the constraint. The algorithms are trained on observational
data using causal inference to learn a policy based on true causal effects. The novel
algorithms are then evaluated and compared to baseline algorithms on synthetic and
real-world antibiotic resistance data.
Beskrivning
Ämne/nyckelord
Machine learning , dynamic programming , causal inference , optimal decision-making