We solve a long-standing problem-determining structural information for disordered materials from their diffraction spectra-for the special case of planar disorder in close-packed structures ͑CPS's͒. Our solution offers the most complete possible statistical description of the disorder and, from it, we find the minimum effective memory length for stacking sequences in CPS's. We contrast this description with the so-called ''fault'' model by comparing the structures inferred using both approaches on two previously published zinc sulphide diffraction spectra.

Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients’ underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural-language processing (NLP) and a knowledge source to differentiate cases in which the patient’s disease is responsible for the event rather than a drug. Our method was applied to 199,920 hospitalization records, concentrating on two serious ADRs: rhabdomyolysis (n = 687) and agranulocytosis (n = 772). Our method automatically identified 75% of the cases, those with disease etiology. The sensitivity and specificity were 93.8% (confidence interval: 88.9-96.7%) and 91.8% (confidence interval: 84.0-96.2%), respectively. The method resulted in considerable saving of time: for every 1 h spent in development, there was a saving of at least 20 h in manual review. The review of the remaining 25% of the cases therefore became more feasible, allowing us to identify the medications that had caused the ADRs.

In previous publications [Varn et al. (2002). Phys. Rev. B, 66, 174110; Varn et al. (2007). Acta Cryst. B63, 169-182] we introduced and applied a new technique for discovering and describing planar disorder in close-packed structures directly from their diffraction patterns. Here, we provide the theoretical development behind those results, adapting computational mechanics to describe one-dimensional structure in materials. We show that the resulting statistical model of the stacking structure - called the ε-machine - allows the calculation of measures of memory, structural complexity and configurational entropy. The methods developed here can be adapted to a wide range of experimental systems in which power spectra data are available.

We apply epsilon-machine spectral reconstruction theory to analyze structure and disorder in four previously published zinc sulfide diffraction spectra and contrast the results with the most common alternative theory, the fault model. In each case we find that the reconstructed epsilon-machine provides a more comprehensive and detailed understanding of the stacking structure, often detecting stacking structures not previously found. Using the epsilon-machines reconstructed for each spectrum, we calculate a number of physical parameters - such as configurational energies, configurational entropies and hexagonality - and several quantities - including statistical complexity and excess entropy - that describe the intrinsic computational properties of the stacking structures.