09.Machine learning11.Sensor driven - sporedata/researchdesigneR GitHub Wiki

1. Use cases: in which situations should I use this method?

Situations with large data volume (e.g., sensor-driven) where there is continuous monitoring of the intervention and the outcome. [1] and [2]

2. Input: what kind of data does the method require?

3. Algorithm: how does the method work?

Model mechanics

Reporting guidelines

Data science packages

Suggested companion methods

Learning materials

4. Output: how do I interpret this method's results?

Mock conclusions, or most frequent format for conclusions reached at the end of a typical analysis.

Tables, plots, and their interpretation

5. SporeData-specific

Templates

Data science functions

References

[1] Corinna Maier, Niklas Hartung, Charlotte Kloft, Wilhelm Huisinga, Jana de Wiljes. Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology.

[2] Komorowski, M., Celi, L.A., Badawi, O. et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med 24, 1716–1720 (2018).

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