BACKGROUND: Coronavirus disease 2019 (COVID-19) altered healthcare utilization patterns. However, there is a dearth of literature comparing methods for quantifying the extent to which the pandemic disrupted healthcare service provision in sub-Saharan African countries. OBJECTIVE: To compare interrupted time series analysis using Prophet and Poisson regression models in evaluating the impact of COVID-19 on essential health services. METHODS: We used reported data from Uganda's Health Management Information System from February 2018 to December 2020. We compared Prophet and Poisson models in evaluating the impact of COVID-19 on new clinic visits, diabetes clinic visits, and in-hospital deliveries between March 2020 to December 2020 and across the Central, Eastern, Northern, and Western regions of Uganda. RESULTS: The models generated similar estimates of the impact of COVID-19 in 10 of the 12 outcome-region pairs evaluated. Both models estimated declines in new clinic visits in the Central, Northern, and Western regions, and an increase in the Eastern Region. Both models estimated declines in diabetes clinic visits in the Central and Western regions, with no significant changes in the Eastern and Northern regions. For in-hospital deliveries, the models estimated a decline in the Western Region, no changes in the Central Region, and had different estimates in the Eastern and Northern regions. CONCLUSIONS: The Prophet and Poisson models are useful in quantifying the impact of interruptions on essential health services during pandemics but may result in different measures of effect. Rigor and multimethod triangulation are necessary to study the true effect of pandemics on essential health services.

BACKGROUND: The onset of COVID-19 and related policy responses made it difficult to study interactive health informatics solutions in clinical study settings. Instrumented log and event data from interactive systems capture temporal details that can be used to generate insights about care continuity during ongoing pandemics. OBJECTIVE: To investigate user interactions with a digital health wallet (DHW) system for addressing care continuity challenges in chronic disease management in the context of an ongoing pandemic. MATERIALS AND METHODS: We analyzed user interaction log data generated by clinicians, nurses, and patients from the deployment of a DHW in a feasibility study conducted during the COVID-19 pandemic in Kenya. We used the Hamming distance from Information Theory to quantify deviations of usage patterns extracted from the events data from predetermined workflow sequences supported by the platform. RESULTS: Nurses interacted with all the user interface elements relevant to triage. Clinicians interacted with only 43% of elements relevant to consultation, while patients interacted with 67% of the relevant user interface elements. Nurses and clinicians deviated from the predetermined workflow sequences by 42% and 36%, respectively. Most deviations pertained to users going back to previous steps in their usage workflow. CONCLUSIONS: User interaction log analysis is a valuable alternative method for generating and quantifying user experiences in the context of ongoing pandemics. However, researchers should mitigate the potential disruptions of the actual use of the studied technologies as well as use multiple approaches to investigate user experiences of health technology during pandemics.

Data-centric AI encourages the need for cleaning, evaluating, and understanding data in order to achieve trustworthy AI. Existing technologies, such as AutoML, make it easier to design and train models automatically, but there is a lack of a similar level of capability to extract data-centric insights. Manual stratification of tabular data per a given feature of interest (e.g., gender) is limited to scaling up for higher feature dimension, which could be addressed using automatic discovery of divergent/anomalous subgroups. Nonetheless, these automatic discovery techniques often search across potentially exponential combinations of features which could be simplified using a preceding feature selection step. Existing feature selection techniques for tabular data often involve fitting a particular model (e.g., XGBoost) in order to select important features. However, such model-based selection is prone to model-bias and spurious correlations in addition to requiring extra resources to design, fine-tune and train a model. In this paper, we propose a model-free and sparsity-based automatic feature selection (SAFS) framework to facilitate automatic discovery of divergent subgroups. Different to filter-based selection techniques, we exploit the sparsity of objective measures among feature values to rank and select features. We validated SAFS across two publicly available datasets (MIMIC-III and Allstate Claims) and compared it with six existing feature selection methods. SAFS achieves a reduction of the feature selection time by a factor of 81× and 104×, averaged cross the existing methods in the MIMIC-III and Claims datasets, respectively. SAFS-selected features are also shown to achieve competitive detection performance, e.g., 18.3% of features selected by SAFS detected similar divergent group compared to using the whole features, in the Claims dataset, with a Jaccard similarity of 0.95 but with a 16× reduction in detection time.

We investigate the effect of variational autoencoder (VAE) based data anonymization and its ability to preserve anomalous subgroup properties. We present a Utility Guaranteed Deep Privacy (UGDP) system which casts existing anomalous pattern detection methods as a new utility measure for data synthesis. UGDP's approach shows that properties of an anomalous subset of records, identified in the original data set, are preserved through the anonymization of a VAE. This is despite the newly generated records being completely synthetic. More specifically, the Bias-Scan algorithm identifies a subgroup of records that are consistently over- (or under-) risked by a black-box classifier as an area of 'poor fit'. This scanning process is applied on both pre- and post- VAE synthesized data. The areas of poor fit (i.e. anomalous records) persist in both settings. We evaluate our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach is able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset.

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving cars, robotics and financial services. We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations. Previous work in this space has shown AE networks can detect anomalous images by thresholding the reconstruction error produced by the final layer. Furthermore, other detection methods rely on data augmentation or specialized training techniques which must be asserted before training time. In contrast, we use subset scanning methods from the anomalous pattern detection domain to enhance detection power without labeled examples of the noise, retraining or data augmentation methods. In addition to an anomalous “score” our proposed method also returns the subset of nodes within the AE network that contributed to that score. This will allow future work to pivot from detection to visualisation and explainability. Our scanning approach shows consistently higher detection power than existing detection methods across several adversarial noise models and a wide range of perturbation strengths.

Improving maternal, newborn, and child health (MNCH) outcomes is a critical target for global sustainable development. Our research is centered on building predictive models, evaluating their interpretability, and generating actionable insights about the markers (features) and triggers (events) associated with vulnerability in MNCH. In this work, we demonstrate how a tool for inspecting "black box" machine learning models can be used to generate actionable insights from models trained on demographic health survey data to predict neonatal mortality.