Abstract:Reinforcement Learning in Healthcare is typically concerned with narrow self-contained tasks such as sepsis prediction or anesthesia control. However, previous research has demonstrated the potential of generalist models (the prime example being Large Language Models) to outperform task-specific approaches due to their capability for implicit transfer learning. To enable training of foundation models for Healthcare as well as leverage the capabilities of state of the art Transformer architectures, we propose the paradigm of Healthcare as Sequence Modeling, in which interaction between the patient and the healthcare provider is represented as an event stream and tasks like diagnosis and treatment selection are modeled as prediction of future events in the stream. To explore this paradigm experimentally we develop MIMIC-SEQ, a sequence modeling benchmark derived by translating heterogenous clinical records from MIMIC-IV dataset into a uniform event stream format, train a baseline model and explore its capabilities.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2402.17501 [cs.LG] |
| (or arXiv:2402.17501v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2402.17501 arXiv-issued DOI via DataCite |
Submission history
From: Vadim Liventsev [view email]
[v1]
Tue, 27 Feb 2024 13:36:55 UTC (1,622 KB)
[v2]
Fri, 24 May 2024 18:50:06 UTC (1,648 KB)
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