Brunner, K., & Hametner, B. (2022). Reviewing Recommender Systems in the Medical Domain. Simulation Notes Europe, 32(4), 203–209. https://doi.org/10.11128/sne.32.tn.10624
E101 - Institut für Analysis und Scientific Computing
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Journal:
Simulation Notes Europe
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ISSN:
2305-9974
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Date (published):
Dec-2022
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Number of Pages:
7
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Peer reviewed:
Yes
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Keywords:
Medical recommender; digital health; JTIAI framework; Thompson Sampling
en
Abstract:
Medical recommender systems are increasing in popularity within the digital health sector. Two main principles for personalised support are just-in-time interventions, and adaptiveness of treatment. Intervention concepts using these principals are called JITAIs, and they aid clients in self-management for health-related issues. In this contribution, the JITAI framework is introduced, and its advantages for recommender systems are discussed. Mathematically, the JITAI concept can be interpreted as a contextual or regular multi-armed bandit problem, which is solved via a bandit algorithm. After discussing several algorithmic strategies of bandit algorithms and elaborating on their differences, the Thompson Sampling strategy is identified as a practical solution for real-life applications using the JTIAI framework. Subsequently, existing recommender systems based on the (contextual) multi-armed bandit approach are reviewed, and the disruption of the algorithm’s learning process by instances of missing data is found to be a prevalent obstacle. An algorithm called Thompson Sampling with Restricted Context is put forward as a solution, where missing data is processed within the bandit setting.