Localized multisensory systems for medical diagnostics are becoming increasingly important due to the prevalence of lightweight sensors and new collection, storage, and communication platforms such as mobile phones and tablets. These systems are often comprised of multisensory arrays that can be inefficient in terms of energy and cost both in sensing and transmission. We make the following two key observations: (i) the raw sensed data itself is unimportant, only those metrics relevant to diagnosis are needed, and (ii) it is often the case that the information relevant to medical diagnosis can be easily derived from the raw sensed data. Therefore, we drive our energy optimization procedure by selecting a subset of sensors that can predict these metrics well, eliminating all others and ultimately reducing the number of required sensors. We also develop a novel procedure for combining adjacent sensors to further reduce cost and sensing energy while increasing prediction strength. Finally, we present an algorithm for sub sampling the selected sensors that leverages the observations that: (i) most sensors need only be sampled after a physiological event is triggered, and (ii) such events tend to be predictable from semantic information and therefore a high sampling rate is unnecessary.