text: This paper presents a new approach to determining what to measure and when to measure it, using a novel deep learning architecture. The problem addressed is important and timely and advances here may have an impact on many application areas outside medicine. The approach is evaluated on real-world medical datasets and has increased accuracy over the other methods compared against. + A key advantage of the approach is that it continually learns from the collected data, using new measurements to update the model, and that it runs efficiently even on large real-world datasets. -However, the related work section is significantly underdeveloped, making it difficult to really compare the approach to the state of the art. The paper is ambitious and claims to address a variety of problems, but as a result each segment of related work seems to have been shortchanged. In particular, the section on missing data is missing a large amount of recent and related work. Normally, methods for handling missing data are categorized based on the missingness model (MAR/MCAR/MNAR). The paper seems to assume all data are missing at random, which is also a significant limitation of the methods. -The paper is organized in a nonstandard way, with the methods split across two sections, separated by the related work. It would be easier to follow with a more common intro/related work/methods structure. Questions: -One of the key motivations for the approach is sensing in medicine. However, many tests come as a group (e.g. the chem-7 or other panels). In this case, even if the only desired measurement is glucose, others will be included as well. Is it possible to incorporate this? It may change the threshold for the decision, as a combination of measures can be obtained for the same cost.