Background Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection. Method Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers’ interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation. Results The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard. Conclusions The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

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Objective: Study the impact of local policies on near-future hospitalization and mortality rates. Materials and Methods:We introduce a novel risk-stratified SIR-HCD model that introduces new variables to model the dynamics of low-contact (e.g., work from home) and high-contact (e.g., work on-site) subpopulations while sharing parameters to control their respective R 0 (t) over time. We test our model on data of daily reported hospitalizations and cumulative mortality of COVID-19 in Harris County, Texas, from May 1, 2020, until October 4, 2020, collected from multiple sources (USA FACTS, U.S. Bureau of Labor Statistics, Southeast Texas Regional Advisory Council COVID-19 report, TMC daily news, and Johns Hopkins University county-level mortality reporting). Results:We evaluated our model's forecasting accuracy in Harris County, TX (the most populated county in the Greater Houston area) during Phase-I and Phase-II reopening. Not only does our model outperform other competing models, but it also supports counterfactual analysis *

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Unnecessary laboratory tests present health risks and increase healthcare costs. We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital. Machine learning models might generate less reliable results due to noisy inputs containing low-quality information. We estimate prediction confidence to measure reliability of predicted results. Using a “select and predict” design philosophy, we aim to maximize prediction performance by selectively considering samples with high prediction confidence for recommendations. We use a conservative definition of unnecessary laboratory tests, which we define as stable and below the lower normal bound (LBNR). Our model accommodates irregularly sampled observational data to make full use of variable correlations (i.e., with other laboratory test values) and temporal dependencies (i.e., previous observations) in order to select candidates for training and prediction. Using data collected from a teaching hospital in Houston, our model achieves Hgb prediction performance with a normality AUC at 95.89% and a Hgb stability AUC at 95.94%, while recommending a reduction of 9.91% of Hgb tests that were deemed unnecessary.