Minimizing search time for finding an effective treatment: Learning a near-optimal policy using constrained algorithms, approximations, and causal inference

dc.contributor.authorHåkansson, Samuel
dc.contributor.authorLindblom, Viktor
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerDubhashi, Devdatt
dc.contributor.supervisorJohansson, Fredrik
dc.date.accessioned2020-07-08T10:18:36Z
dc.date.available2020-07-08T10:18:36Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractPatients 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.sv
dc.identifier.coursecodeDATX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301394
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectMachine learningsv
dc.subjectdynamic programmingsv
dc.subjectcausal inferencesv
dc.subjectoptimal decision-makingsv
dc.titleMinimizing search time for finding an effective treatment: Learning a near-optimal policy using constrained algorithms, approximations, and causal inferencesv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
CSE 20-54 Håkansson Lindblom.pdf
Storlek:
2.44 MB
Format:
Adobe Portable Document Format
Beskrivning:
License bundle
Visar 1 - 1 av 1
Bild saknas
Namn:
license.txt
Storlek:
1.14 KB
Format:
Item-specific license agreed upon to submission
Beskrivning: