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Abstract Objective Modern healthcare data reflect massive multi-level and multi-scale information collected over many years. The majority of the existing phenotyping algorithms use case–control definitions of disease. This paper aims to study the time to disease onset and progression and identify the time-varying risk factors that drive them. Materials and Methods We developed an algorithmic approach to phenotyping the incidence of diseases by consolidating data sources from the UK Biobank (UKB), including primary care electronic health records (EHRs). We focused on defining events, event dates, and their censoring time, including relevant terms and existing phenotypes, excluding generic, rare, or semantically distant terms, forward-mapping terminology terms, and expert review. We applied our approach to phenotyping diabetes complications, including a composite cardiovascular disease (CVD) outcome, diabetic kidney disease (DKD), and diabetic retinopathy (DR), in the UKB study. Results We identified 49 049 participants with diabetes. Among them, 1023 had type 1 diabetes (T1D), and 40 193 had type 2 diabetes (T2D). A total of 23 833 diabetes subjects had linked primary care records. There were 3237, 3113, and 4922 patients with CVD, DKD, and DR events, respectively. The risk prediction performance for each outcome was assessed, and our results are consistent with the prediction area under the ROC (receiver operating characteristic) curve (AUC) of standard risk prediction models using cohort studies. Discussion and Conclusion Our publicly available pipeline and platform enable streamlined curation of incidence events, identification of time-varying risk factors underlying disease progression, and the definition of a relevant cohort for time-to-event analyses. These important steps need to be considered simultaneously to study disease progression. 

Measuring the fluctuating static pressure within a jet has the potential to depict in-flow sources of the jet noise. In this work, the fluctuating static pressure of a subsonic axisymmetric jet was experimentally investigated using a 1/8” microphone with an aerodynamically shaped nose cone. The power spectra of the fluctuating pressure are found to follow the -7/3 scaling law at the jet centerline with the decay rate varying as the probe approaches the acoustic near field. Profiles of skewness and kurtosis reveal strong intermittency inside the jet shear layer. By applying a continuous wavelet transform (CWT), time-localized footprints of the acoustic sources were detected from the pressure fluctuations. To decompose the fluctuating pressure into the hydrodynamic component and its acoustic counterpart, two techniques based on the CWT are adopted. In the first method the hydrodynamic pressure is isolated by maximizing the correlation with the synchronously measured turbulent velocity, while the second method originates from the Gaussian nature of the acoustic pressure where the separation threshold is determined empirically. Similar results are obtained from both separation techniques, and each pressure component dominates a certain frequency band compared to the global spectrum. Furthermore, cross-spectra between the fluctuating pressure and the turbulent velocity were calculated, and spectral peaks appearing around Strouhal number of 0.4 are indicative of the footprint of the convecting coherent structures inside the jet mixing layer.