Uncovering otherwise hidden clinically valuable knowledge from routinely monitored and commonly discarded data is the objective of intelligent biosignal analysis. In any Intensive Care Unit (ICU), a bedside monitor routinely provides a continuous display of waveforms and point-intime display of vital signs. Typically, hundreds of data points are recorded per second (125 per waveform), however care providers typically chart hourly and detect changes over several hours. The inefficient waste is remarkable, that is, if one believes there is valuable information hidden within the waveform. Addressing this challenge, there is a growing and exciting science driven by the desire to transform the way waveforms are analyzed, improve the value of monitoring, and thus improve quality and efficiency of care. In interpreting this editorial regarding the accompanying article by Lake and colleagues [1], it is important to disclose my bias and conflict of interest regarding this work and subject matter. I have been long pursuing the development of software to perform multi organ variability monitoring and testing it clinically, seeking to understand the physiology of altered variability, and pursuing the commercial objective of introducing variability-based products to ICUs. Thus, I am biased based on time invested, potential for financial benefit, and my belief system; I do this work because of underlying beliefs that this approach could help provide clinically meaningful information to the bedside to improve care for our patients. Thus, please interpret these comments as you see fit. To uncover clinically meaningful information from waveforms (e.g. electrocardiogram, capnography), two steps are required. The first is waveform analytics. The anatomy of a waveform must be dissected and measured per beat or breath. Data derived from waveform anatomy per breath such as ST-depression and end-tidal CO2 monitoring are routinely available and commonly clinically used. A second level of analysis involves the integration of information over intervals-of-time, with analyses degree and character of variation of the time series of events captured by the waveform (e.g. inter-beat and inter-breath intervals). Variability analyses include measures of overall variation, patterns of accelerations and decelerations, presence of high and low frequency variation, scale-free correlations, and more. All of these measures are nonetheless derived from analyzing waveforms. To transform these measures into clinically useful information, one needs a fundamentally distinct second step, namely populationderived predictive modeling. In contrast to waveform analytics, predictive modeling requires the time consuming and expensive process of enrolling patients into large studies, recording outcomes, as well as measuring the waveform analytics in a reproducible, standardized fashion. Thus, the combination of biomedical engineering and statistical modeling are employed in the act of uncovering clinical decision support from waveform biosignals to and from the bedside. Guiding the way in this domain of bedside delivery of complex signal bioinformatics is a group from the University of Virginia, led by Randall Moorman and Douglas Lake. They sought to improve neonatal critical care monitoring by developing automated methods to uncover This Editorial refers to the article available at doi: 10.1007/s10877013-9530-x.