Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown.

This paper reports the use of simulated annealing to design more efficient fuzzy logic\ud systems to model problems with associated uncertainties. Simulated annealing is used\ud within this work as a method for learning the best configurations of interval and general\ud type-2 fuzzy logic systems to maximize their modeling ability. The combination\ud of simulated annealing with these models is presented in the modeling of four benchmark\ud problems including real-world problems. The type-2 fuzzy logic system models\ud are compared in their ability to model uncertainties associated with these problems.\ud Issues related to this combination between simulated annealing and fuzzy logic systems,\ud including type-2 fuzzy logic systems, are discussed. The results demonstrate that\ud learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can\ud add more capability to modeling than interval type-2 fuzzy logic systems. This finding\ud can be seen as an important advance in type-2 fuzzy logic systems research and should\ud increase the level of interest in the modeling applications of general type-2 fuzzy logic\ud systems, despite their greater computational load

Transmission electron microscopy of GaAs=Inx Ga1 x As=GaAs double heterostructures has enabled the onset and subsequent development of misfit dislocations to be followed for increasing strained-layer thicknesses, from sub- to supercritical. It has been observed that misfit segments are introduced into threading dislocations when the strained-layer thickness is close to, but below, the critical thickness predicted by the Matthews-Blakeslee (M-B) model. Analysis shows that threading dislocations may be able to glide to form interfacial misfit dislocation segments even though the critical thickness predicted by the M-B model has not been reached. It has also been observed that the total dislocation density rises slowly as the layer thickness increases above its critical value, until a sudden increase occurs. It is suggested that the sudden increase in dislocation density is associated with a different mechanism of misfit dislocation formation, which dominates the global relaxation of the structure.

Data analytics and artificial intelligence (AI) have been used to predict patient outcomes after colorectal cancer surgery. A prospectively maintained colorectal cancer database was used, covering 4336 patients who underwent colorectal cancer surgery between 2003 and 2019. The 47 patient parameters included demographics, peri- and post-operative outcomes, surgical approaches, complications, and mortality. Data analytics were used to compare the importance of each variable and AI prediction models were built for length of stay (LOS), readmission, and mortality. Accuracies of at least 80% have been achieved. The significant predictors of LOS were age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications. The model with support vector regression (SVR) algorithms predicted the LOS with an accuracy of 83% and mean absolute error (MAE) of 9.69 days. The significant predictors of readmission were age, laparoscopic procedure, stoma performed, preoperative nodal (N) stage, operation time, operation mode, previous surgery type, LOS, and the specific procedure. A BI-LSTM model predicted readmission with 87.5% accuracy, 84% sensitivity, and 90% specificity. The significant predictors of mortality were age, ASA grade, BMI, the formation of a stoma, preoperative TNM staging, neoadjuvant chemotherapy, curative resection, and LOS. Classification predictive modelling predicted three different colorectal cancer mortality measures (overall mortality, and 31- and 91-days mortality) with 80–96% accuracy, 84–93% sensitivity, and 75–100% specificity. A model using all variables performed only slightly better than one that used just the most significant ones.