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Purpose This study aimed to develop real-world synthetic electronic health record (EHR) for emergency departments using computationally efficient and stable diffusion probabilistic models. Methods In this study, we compared the performance of diffusion models and state-ofthe-art generative adversarial networks (GANs) in terms of statistical similarity, privacy, medical usefulness, and the feasibility of using synthetic data for machine learning purposes. Results Our results demonstrate that diffusion models are significantly more computationally efficient than GANs and perform comparably or slightly better in terms of similarity, privacy, and utility. We also found that the data quality of the diffusion model is statistically very similar for both categorical and continuous values and can address class imbalance precisely. Moreover, the usefulness of synthetic data is almost identical to that of real EHR data. Our privacy analysis showed that the synthetic data generated by the diffusion models were private. Conclusion These findings have significant implications for improving the efficiency of emergency settings and enabling real-time emergency room data modeling. This demonstrates the potential of diffusion models for generating computationally efficient high-quality synthetic data. The study concluded that diffusion models can generate real-world synthetic EHRs that are computationally efficient, private, and high-quality, and can be used for machine learning purposes in emergency settings.

Abstract Privacy concerns often arise as the key bottleneck for the sharing of data between consumers and data holders, particularly for sensitive data such as Electronic Health Records (EHR). This impedes the application of data analytics and ML-based innovations with tremendous potential. One promising approach for such privacy concerns is to instead use synthetic data. We propose a generative modeling framework, EHR-Safe, for generating highly realistic and privacy-preserving synthetic EHR data. EHR-Safe is based on a two-stage model that consists of sequential encoder-decoder networks and generative adversarial networks. Our innovations focus on the key challenging aspects of real-world EHR data: heterogeneity, sparsity, coexistence of numerical and categorical features with distinct characteristics, and time-varying features with highly-varying sequence lengths. Under numerous evaluations, we demonstrate that the fidelity of EHR-Safe is almost-identical with real data (

Background Empiric antimicrobial therapy with azithromycin is highly used in patients admitted to the hospital with COVID‐19, despite prior research suggesting that azithromycin may be associated with increased risk of cardiovascular events. Methods and Results This study was conducted using data from the ISACS‐COVID‐19 (International Survey of Acute Coronavirus Syndromes‐COVID‐19) registry. Patients with a confirmed diagnosis of SARS‐CoV‐2 infection were eligible for inclusion. The study included 793 patients exposed to azithromycin within 24 hours from hospital admission and 2141 patients who received only standard care. The primary exposure was cardiovascular disease (CVD). Main outcome measures were 30‐day mortality and acute heart failure (AHF). Among 2934 patients, 1066 (36.4%) had preexisting CVD. A total of 617 (21.0%) died, and 253 (8.6%) had AHF. Azithromycin therapy was consistently associated with an increased risk of AHF in patients with preexisting CVD (risk ratio [RR], 1.48 [95% CI, 1.06–2.06]). Receiving azithromycin versus standard care was not significantly associated with death (RR, 0.94 [95% CI, 0.69–1.28]). By contrast, we found significantly reduced odds of death (RR, 0.57 [95% CI, 0.42–0.79]) and no significant increase in AHF (RR, 1.23 [95% CI, 0.75–2.04]) in patients without prior CVD. The relative risks of death from the 2 subgroups were significantly different from each other (Pinteraction=0.01). Statistically significant association was observed between AHF and death (odds ratio, 2.28 [95% CI, 1.34–3.90]). Conclusions These findings suggest that azithromycin use in patients with COVID‐19 and prior history of CVD is significantly associated with an increased risk of AHF and all‐cause 30‐day mortality. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT05188612.

Abstract Racial and ethnic minorities have borne a particularly acute burden of the COVID-19 pandemic in the United States. There is a growing awareness from both researchers and public health leaders of the critical need to ensure fairness in forecast results. Without careful and deliberate bias mitigation, inequities embedded in data can be transferred to model predictions, perpetuating disparities, and exacerbating the disproportionate harms of the COVID-19 pandemic. These biases in data and forecasts can be viewed through both statistical and sociological lenses, and the challenges of both building hierarchical models with limited data availability and drawing on data that reflects structural inequities must be confronted. We present an outline of key modeling domains in which unfairness may be introduced and draw on our experience building and testing the Google-Harvard COVID-19 Public Forecasting model to illustrate these challenges and offer strategies to address them. While targeted toward pandemic forecasting, these domains of potentially biased modeling and concurrent approaches to pursuing fairness present important considerations for equitable machine-learning innovation.

Monitoring players’ fatigue is essential to maintaining the best performance of players during sports games. The level of fatigue can be measured by the external workload, the aggregated amount of physical activity or internal workload, which is an individual’s psycho-physiological response to that activity. There have been a growing number of studies focusing on the relationship between external and internal workloads for efficient fatigue monitoring. However, they utilize aggregated features to represent the external workload, losing raw data details such as sequential information. This study proposes a deep learning algorithm to predict Rate of Perceived Exertion (RPE) from players’ movement data instead of aggregated features. Electronic Performance and Tracking Systems (EPTS) powered by GPS sensors collected players’ movement data and the RPE from training and match sessions during a Korean professional soccer team season. We preprocessed the raw GPS data to obtain linear and angular components of velocity, acceleration, and jerk. Our proposed model, named FatigueNet, effectively predicted the RPE with mean absolute error (MAE) = 0.8494 ± 0.0557 and root mean square error (RMSE) = 1.2166 ± 0.0737 using the preprocessed movement features. To interpret the predictions of the FatigueNet, we also performed regression activation mapping to localize the discriminative time intervals that contributed more to the prediction results. Our experimental results imply the possibility of automated and objective fatigue monitoring systems based on deep learning instead of arduous manual data collection from players.

Abstract The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases, and hospitalizations during the following 4 weeks. We present an international, prospective evaluation of our models’ performance across all states and counties in the USA and prefectures in Japan. Nationally, incident mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths during prospective deployment remained consistently

Abstract The inertial measurement unit (IMU) and magnetic, angular rate, and gravity (MARG) sensor orientation and position are widely used in the medical, robotics, and other fields. In general, the orientations can be defined by the integration of angular velocity data, and the positions are also computed from the double integration of acceleration data. However, the acceleration and angular velocity data are often inaccurate due to measurement errors which arise when the sensor moves quickly. Therefore, the orientations and positions significantly differ from the actual values. To address these issues, several techniques are proposed for the accurate measurement of IMU and MARG sensor orientations and positions. The proposed optimization method is applied to raw sensor data to compute faithful orientations by stabilizing and accelerating the convergence of the optimization process. Furthermore, a deep neural network based on 1D convolutional neural network (CNN) layers is proposed to predict the desired velocity from raw acceleration data. The method is validated qualitatively and quantitatively with an optical motion capture (mocap) system. The experimental results show that the proposed method significantly improves orientation and position estimations compared to those of other approaches.

This study presents the filter design of GNSS/IMU integration for wearable EPTS (Electronic Performance and Tracking System) of football players. EPTS has been widely used in sports fields recently, and GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) in wearable EPTS have been used to measure and provide players’ athletic performance data. A sensor fusion technique can be used to provide high-quality analysis data of athletic performance. For this reason, the integration filter of GNSS data and IMU data is designed in this study. The loosely-coupled strategy is considered to integrate GNSS and IMU data considering the specification of the wearable EPTS product. Quaternion is used to estimate a player’s attitude to avoid the gimbal lock singularity in this study. Experiment results validate the performance of the proposed GNSS/IMU loosely-coupled integration filter for wearable EPTS of football players.

Background It is still unknown whether traditional risk factors may have a sex‐specific impact on coronary artery disease (CAD) burden. Methods and Results We identified 14 793 patients who underwent coronary angiography for acute coronary syndromes in the ISACS‐TC (International Survey of Acute Coronary Syndromes in Transitional Countries; Clini​calTr​ials.gov, NCT01218776) registry from 2010 to 2019. The main outcome measure was the association between traditional risk factors and severity of CAD and its relationship with 30‐day mortality. Relative risk (RR) ratios and 95% CIs were calculated from the ratio of the absolute risks of women versus men using inverse probability of weighting. Estimates were compared by test of interaction on the log scale. Severity of CAD was categorized as obstructive (≥50% stenosis) versus nonobstructive CAD. The RR ratio for obstructive CAD in women versus men among people without diabetes mellitus was 0.49 (95% CI, 0.41–0.60) and among those with diabetes mellitus was 0.89 (95% CI, 0.62–1.29), with an interaction by diabetes mellitus status of P =0.002. Exposure to smoking shifted the RR ratios from 0.50 (95% CI, 0.41–0.61) in nonsmokers to 0.75 (95% CI, 0.54–1.03) in current smokers, with an interaction by smoking status of P=0.018. There were no significant sex‐related interactions with hypercholesterolemia and hypertension. Women with obstructive CAD had higher 30‐day mortality rates than men (RR, 1.75; 95% CI, 1.48–2.07). No sex differences in mortality were observed in patients with nonobstructive CAD. Conclusions Obstructive CAD in women signifies a higher risk for mortality compared with men. Current smoking and diabetes mellitus disproportionally increase the risk of obstructive CAD in women. Achieving the goal of improving cardiovascular health in women still requires intensive efforts toward further implementation of lifestyle and treatment interventions. Registration URL: https://www.clini​caltr​ials.gov; Unique identifier: NCT01218776.

Background: Controversy exists as to whether low-dose aspirin use may give benefit in primary prevention of cardiovascular (CV) events. We hypothesized that the benefits of aspirin are underevaluated. Methods: We investigated 12,123 Caucasian patients presenting to hospital with acute coronary syndromes as first manifestation of CV disease from 2010 to 2019 in the ISACS-TC multicenter registry (ClinicalTrials.gov, NCT01218776). Individual risk of ST segment elevation myocardial infarction (STEMI) and its association with 30-day mortality was quantified using inverse probability of treatment weighting models matching for concomitant medications. Estimates were compared by test of interaction on the log scale. Findings: The risk of STEMI was lower in the aspirin users (absolute reduction: 6·8%; OR: 0·73; 95%CI: 0·65–0·82) regardless of sex (p for interaction=0·1962) or age (p for interaction=0·1209). Benefits of aspirin were seen in patients with hypertension, hypercholesterolemia, and in smokers. In contrast, aspirin failed to demonstrate a significant risk reduction in STEMI among diabetic patients (OR:1·10;95%CI:0·89–1·35) with a significant interaction (p:

Background We hypothesized that female sex is a treatment effect modifier of blood flow and related 30‐day mortality after primary percutaneous coronary intervention (PCI) for ST‐segment–elevation myocardial infarction and that the magnitude of the effect on outcomes differs depending on delay to hospital presentation. Methods and Results We identified 2596 patients enrolled in the ISACS‐TC (International Survey of Acute Coronary Syndromes in Transitional Countries) registry from 2010 to 2016. Primary outcome was the occurrence of 30‐day mortality. Key secondary outcome was the rate of suboptimal post‐PCI Thrombolysis in Myocardial Infarction (TIMI; flow grade 0–2). Multivariate logistic regression and inverse probability of treatment weighted models were adjusted for baseline clinical covariates. We characterized patient outcomes associated with a delay from symptom onset to hospital presentation of ≤120 minutes. In multivariable regression models, female sex was associated with postprocedural TIMI flow grade 0 to 2 (odds ratio [OR], 1.68; 95% CI, 1.15–2.44) and higher mortality (OR, 1.72; 95% CI, 1.02–2.90). Using inverse probability of treatment weighting, 30‐day mortality was higher in women compared with men (4.8% versus 2.5%; OR, 2.00; 95% CI, 1.27–3.15). Likewise, we found a significant sex difference in post‐PCI TIMI flow grade 0 to 2 (8.8% versus 5.0%; OR, 1.83; 95% CI, 1.31–2.56). The sex gap in mortality was no longer significant for patients having hospital presentation of ≤120 minutes (OR, 1.28; 95% CI, 0.35–4.69). Sex difference in post‐PCI TIMI flow grade was consistent regardless of time to hospital presentation. Conclusions Delay to hospital presentation and suboptimal post‐PCI TIMI flow grade are variables independently associated with excess mortality in women, suggesting complementary mechanisms of reduced survival. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01218776.

Risk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015.We develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs discovers these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art machine learning methods, our method provides significant improvements in survival predictions, both post- and pre-cardiac transplantation. For instance: in terms of 3-month survival post-transplantation, our method achieves AUC of 0.660; the best clinical risk scoring method (RSS) achieves 0.587. In terms of 3-year survival/mortality predictions post-transplantation (in comparison to RSS), holding specificity at 80.0%, our algorithm correctly predicts survival for 2,442 (14.0%) more patients (of 17,441 who actually survived); holding sensitivity at 80.0%, our algorithm correctly predicts mortality for 694 (13.0%) more patients (of 5,339 who did not survive). ToPs achieves similar improvements for other time horizons and for predictions pre-transplantation. ToPs discovers the most relevant features (covariates), uses available features to best advantage, and can adapt to changes in clinical practice.We show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching.