The impact of electronic health records (EHR) data continuity on prediction model fairness and racial-ethnic disparities

Electronic health records (EHR) data have considerable variability in data completeness across sites and patients. Lack of "EHR data-continuity" or "EHR data-discontinuity", defined as "having medical information recorded outside the reach of an EHR system" can lead to a substantial amount of information bias. The objective of this study was to comprehensively evaluate (1) how EHR data-discontinuity introduces data bias, (2) case finding algorithms affect downstream prediction models, and (3) how algorithmic fairness is associated with racial-ethnic disparities. We leveraged our EHRs linked with Medicaid and Medicare claims data in the OneFlorida+ network and used a validated measure (i.e., Mean Proportions of Encounters Captured [MPEC]) to estimate patients' EHR data continuity. We developed a machine learning model for predicting type 2 diabetes (T2D) diagnosis as the use case for this work. We found that using cohorts selected by different levels of EHR data-continuity affects utilities in disease prediction tasks. The prediction models trained on high continuity data will have a worse fit on low continuity data. We also found variations in racial and ethnic disparities in model performances and model fairness in models developed using different degrees of data continuity. Our results suggest that careful evaluation of data continuity is critical to improving the validity of real-world evidence generated by EHR data and health equity

Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)

Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is therefore crucial to implement effective social risk management strategies at the point of care. Objective: To develop an EHR-based machine learning (ML) analytical pipeline to identify the unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the University of Florida Health Integrated Data Repository, including contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing stability). We developed an electronic health records (EHR)-based machine learning (ML) analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) techniques and fairness assessment and optimization. Results: Our iPsRS achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial-ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk; the actual 1-year hospitalization rate in the top 5% of iPsRS was ~13 times as high as the bottom decile. Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in T2D patients

Ice-nucleating particles from multiple aerosol sources in the urban environment of Beijing under mixed-phase cloud conditions

Ice crystals occurring in mixed-phase clouds play a vital role in global precipitation and energy balance because of the unstable equilibrium between coexistent liquid droplets and ice crystals, which affects cloud lifetime and radiative properties, as well as precipitation formation. Satellite observations proved that immersion freezing, i.e., ice formation on particles immersed within aqueous droplets, is the dominant ice nucleation (IN) pathway in mixed-phase clouds. However, the impact of anthropogenic emissions on atmospheric IN in the urban environment remains ambiguous. In this study, we present in situ observations of ambient ice-nucleating particle number concentration (NINP) measured at mixed-phase cloud conditions (−30 ∘C, relative humidity with respect to liquid water RHw= 104 %) and the physicochemical properties of ambient aerosol, including chemical composition and size distribution, at an urban site in Beijing during the traditional Chinese Spring Festival. The impact of multiple aerosol sources such as firework emissions, local traffic emissions, mineral dust, and urban secondary aerosols on NINP is investigated. The results show that NINP during the dust event reaches up to 160 # L−1 (where “#” represents number of particles), with an activation fraction (AF) of 0.0036 % ± 0.0011 %. During the rest of the observation, NINP is on the order of 10−1 to 10 # L−1, with an average AF between 0.0001 % and 0.0002 %. No obvious dependence of NINP on the number concentration of particles larger than 500 nm (N500) or black carbon (BC) mass concentration (mBC) is found throughout the field observation. The results indicate a substantial NINP increase during the dust event, although the observation took place at an urban site with high background aerosol concentration. Meanwhile, the presence of atmospheric BC from firework and traffic emissions, along with urban aerosols formed via secondary transformation during heavily polluted periods, does not influence the observed INP concentration. Our study corroborates previous laboratory and field findings that anthropogenic BC emission has a negligible effect on NINP and that NINP is unaffected by heavy pollution in the urban environment under mixed-phase cloud conditions.</p

Towards Trustworthy Artificial Intelligence for Equitable Global Health

Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustworthy AI in global health, we convened a workshop on Fairness in Machine Intelligence for Global Health (FairMI4GH). The event brought together a global mix of experts from various disciplines, community health practitioners, policymakers, and more. Topics covered included managing AI bias in socio-technical systems, AI's potential impacts on global health, and balancing data privacy with transparency. Panel discussions examined the cultural, political, and ethical dimensions of AI in global health. FairMI4GH aimed to stimulate dialogue, facilitate knowledge transfer, and spark innovative solutions. Drawing from NIST's AI Risk Management Framework, it provided suggestions for handling AI risks and biases. The need to mitigate data biases from the research design stage, adopt a human-centered approach, and advocate for AI transparency was recognized. Challenges such as updating legal frameworks, managing cross-border data sharing, and motivating developers to reduce bias were acknowledged. The event emphasized the necessity of diverse viewpoints and multi-dimensional dialogue for creating a fair and ethical AI framework for equitable global health.Comment: 7 page

Blood pressure and cardiovascular disease in type 1 diabetes: an exploration of prediction and control

This dissertation provides, in a type 1 diabetes (T1D) cohort followed for 25 years, a comprehensive examination of both blood pressure (BP) as a cardiovascular disease risk predictor and the role of the renin-angiotensin system (RAS) inhibition in reducing cardiovascular risk. Data are from the Pittsburgh Epidemiology of Diabetes Complications (EDC) Study of childhood-onset diabetes. First, we observed that all five BP indices (systolic [SBP], diastolic [DBP], pulse [PP], mean arterial [MAP] and mid-blood pressure [MidBP]) predicted incident coronary artery disease (CAD) independently of other risk factors. Although PP was less effective in the entire cohort, its prognostic significance improved, and became comparable to SBP, in participants age 35 years and older and/or with poor glycemic control. This likely reflects an early onset of glycation-included vascular stiffening in T1D. Second, using time-weighted variables that reflected long-term exposure to high BP from youth throughout midlife, we found dose-gradient associations of SBP, DBP and MAP with CAD outcomes, beginning at approximately 120, 80 and 90 mmHg, respectively. This suggests a lower BP goal (i.e.,120/80 mmHg) is needed than currently recommended (140/90 mmHg) for young T1D adults. In the third analysis, an examination of the RAS inhibition effect on CAD outcomes in T1D, appropriate statistical methods (inverse probability treatment weight, marginal structural model, and causal mediation analysis) were used under a causal-inference framework. RAS inhibitors, but not β blockers or calcium channel blockers, reduced CAD risk, though the results did not reach statistical significance. Mediation analysis indicated that cardiovascular protective effect of RAS inhibitors was partially achieved through pathways beyond lowering BP and urinary albumin, the two prominent effects of this antihypertensive class. Though not significant, these findings suggest a greater potential for RAS inhibitors to offer superior cardioprotection, compared to β blockers and calcium channel blockers, in T1D. Overall, the dissertation findings have contributed to filling some critical gaps in our understanding of the magnitude of cardiovascular risk associated with BP and how to effectively control hypertension in T1D. This body of work thus has important public health relevance, given the enormous contribution of cardiovascular disease to T1D mortality and morbidity

Research on the Extraction of Hazard Sources along High-Speed Railways from High-Resolution Remote Sensing Images Based on TE-ResUNet

There are many potential hazard sources along high-speed railways that threaten the safety of railway operation. Traditional ground search methods are failing to meet the needs of safe and efficient investigation. In order to accurately and efficiently locate hazard sources along the high-speed railway, this paper proposes a texture-enhanced ResUNet (TE-ResUNet) model for railway hazard sources extraction from high-resolution remote sensing images. According to the characteristics of hazard sources in remote sensing images, TE-ResUNet adopts texture enhancement modules to enhance the texture details of low-level features, and thus improve the extraction accuracy of boundaries and small targets. In addition, a multi-scale Lov&aacute;sz loss function is proposed to deal with the class imbalance problem and force the texture enhancement modules to learn better parameters. The proposed method is compared with the existing methods, namely, FCN8s, PSPNet, DeepLabv3, and AEUNet. The experimental results on the GF-2 railway hazard source dataset show that the TE-ResUNet is superior in terms of overall accuracy, F1-score, and recall. This indicates that the proposed TE-ResUNet can achieve accurate and effective hazard sources extraction, while ensuring high recall for small-area targets

GSDMD knockdown attenuates phagocytic activity of microglia and exacerbates seizure susceptibility in TLE mice

Abstract Background Temporal lobe epilepsy (TLE) is often characterized pathologically by severe neuronal loss in the hippocampus. Phagocytic activity of microglia is essential for clearing apoptotic neuronal debris, allowing for repair and regeneration. Our previous research has shown that gasdermin D (GSDMD)-mediated pyroptosis is involved in the pathogenesis of TLE. However, whether GSDMD-mediated pyroptosis influences the accumulation of apoptotic neurons remains unclear. Therefore, the present study was designed to investigate whether phagocytic activity of microglia is involved in GSDMD-mediated pyroptosis and the pathogenesis of TLE. Methods To establish a TLE model, an intra-amygdala injection of kainic acid (KA) was performed. The Racine score and local field potential (LFP) recordings were used to assess seizure severity. Neuronal death in the bilateral hippocampus was assessed by Nissl staining and TUNEL staining. Microglial morphology and phagocytic activity were detected by immunofluorescence and verified by lipopolysaccharide (LPS) and the P2Y12R agonist 2MeSADP. Results GSDMD knockdown augmented the accumulation of apoptotic neurons and seizure susceptibility in TLE mice. Microglia activated and transition to the M1 type with increased pro-inflammatory cytokines. Furthermore, GSDMD knockdown attenuated the migration and phagocytic activity of microglia. Of note, LPS-activated microglia attenuated seizure susceptibility and the accumulation of apoptotic neurons in TLE after GSDMD knockdown. A P2Y12R selective agonist, 2MeSADP, enhanced the migration and phagocytic activity of microglia. Conclusions Our results demonstrate that GSDMD knockdown exacerbates seizure susceptibility and the accumulation of apoptotic neurons by attenuating phagocytic activity of microglia. These findings suggest that GSDMD plays a protective role against KA-induced seizure susceptibility

Predicting the Risk of Alzheimer’s Disease and Related Dementia in Patients with Mild Cognitive Impairment Using a Semi-Competing Risk Approach

Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) to mild cognitive impairment (MCI) due to AD (i.e., mild symptoms but not interfere with daily activities), followed by increasing severity of dementia due to AD. Early detection and prediction models for the transition of MCI to AD/ADRD are needed, and efforts have been made to build predictions of MCI conversion to AD/ADRD. However, most existing studies developing such prediction models did not consider the competing risks of death, which may result in biased risk estimates. In this study, we aim to develop a prediction model for AD/ADRD among patients with MCI considering the competing risks of death using a semi-competing risk approach

Earth-Mover-Distance-Based Detection of False Data Injection Attacks in Smart Grids

The high integration of power information physical system improves the efficiency of power transmission, but it also brings new threats to power grid. False data injection attacks can use traditional bad data to detect vulnerabilities and maliciously tamper with measurement data to affect the state estimation results. In order to achieve a higher security level for power systems, we propose an earth mover distance method to detect false data injection attacks in smart grids. The proposed method is built on the dynamic correlation of measurement data between adjacent moments. Firstly, a joint-image-transformation-based scheme is proposed to preprocess the measurement data variation, so that the distribution characteristics of measurement data variation are more significant. Secondly, the deviation between the probability distribution of measurement data variation and the histogram are obtained based on the earth’s mover distance. Finally, a reasonable detection threshold is selected to judge whether there are false data injection attacks. The proposed method is tested using IEEE 14 bus system considering the state variable attacks on different nodes. The results verified that the proposed method has a high detection accuracy against false data injection attacks