Forecasting Time Series in Healthcare With Gaussian Processes and Dynamic Time Warping Based Subset Selection.

Modelling real-world time series can be challenging in the absence of sufficient data. Limited data in healthcare, can arise for several reasons, namely when the number of subjects is insufficient or the observed time series is irregularly sampled at a very low sampling frequency. This is especially true when attempting to develop personalised models, as there are typically few data points available for training from an individual subject.

Discovery, validation, and diagnostic ability of multiple protein-based biomarkers in saliva and gingival crevicular fluid to distinguish between health and periodontal diseases.

Aim: To discover and validate differential protein biomarker expression in saliva and gingival crevicular fluid (GCF) to discriminate objectively between periodontal health and plaque-induced periodontal disease states.

Materials And Methods: One-hundred and ninety participants were recruited from two centres (Birmingham and Newcastle upon Tyne, UK) comprising healthy, gingivitis, periodontitis, and edentulous donors. Samples from the Birmingham cohort were analysed by quantitative mass spectrometry proteomics for biomarker discovery.

Gestational weight gain prediction using privacy preserving federated learning.

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data.

Feature selection for unbiased imputation of missing values: A case study in healthcare.

Datasets in healthcare are plagued with incomplete information. Imputation is a common method to deal with missing data where the basic idea is to substitute some reasonable guess for each missing value and then continue with the analysis as if there were no missing data. However unbiased predictions based on imputed datasets can only be guaranteed when the missing mechanism is completely independent of the observed or missing data.

PREgDICT : Early prediction of gestational weight gain for pregnancy care.

Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity.

Finite difference time domain electroacoustic model for synthetic jet actuators including nonlinear flow resistance.

A lumped element electroacoustic model for a synthetic jet actuator is presented. The model includes the nonlinear flow resistance associated with flow separation and employs a finite difference scheme in the time domain. As opposed to more common analytical frequency domain electroacoustic models, in which the nonlinear resistance can only be considered as a constant, it allows the calculation of higher harmonics, i.