Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach.

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO2 was 16.7 µg/m3, equals to a drop of 73% in NO2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO2 was 3.1 µg/m3, accounting for a marginal reduction of 18.6% of the overall NO2 change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 µg/m3 NO2 per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO2 concentration into compliance with the health-based limit.

Emergency Department Patient Navigator Program Demonstrates Reduction in Emergency Department Return Visits and Increase in Follow-up Appointment Adherence.

BACKGROUND: An estimated 56% of emergency department (ED) visits are avoidable. One motivation for return visits is patients' perception of poor access to timely outpatient care. Efforts to facilitate access may help reduce preventable ED visits. We aimed to analyze whether an ED patient navigator (PN) program improved adherence with outpatient appointments and reduced ED return visits. METHODS: We performed a retrospective analysis of patients evaluated and discharged from two EDs from October 2016 to December 2019. Using propensity score matching, an intervention case group was matched against two control groups - patients similar to the case group who presented either (1) pre-PN intervention or (2) post-PN intervention and did not receive intervention. The four outcomes included 72-h return ED visits, 30-day return ED visits, overall ED utilization, as well as the intervention group's adherence rates to PN-scheduled outpatient appointments. From 482,896 charts, propensity matching led to a total of 14,295 patients in each group. RESULTS: PN intervention decreased both acute and subacute ED return visits. Compared to both pre-PN and post-PN controls, navigated patients had a decrease in 72-h and 30-day return visits from 2% to 1% and 7% to 4% (p < 0.001) respectively. Navigated patients also had outpatient appointment adherence rates of 74-80% compared to the estimated national average of 25-56%. While there was no difference in mean ED utilization between the intervention group and pre-PN control group, mean ED utilization was found to be higher in the intervention group compared to the post-PN control group with 0.62 visits compared to 0.38 mean visits (p < 0.001). CONCLUSIONS: By facilitating access to post-ED care, PNs may reduce avoidable ED utilization and improve outpatient follow-up adherence. While overall ED utilization did not change, this may be due to the overall vulnerability of the navigated group which is the goal PN intervention group.

Estimation of postpartum depression risk from electronic health records using machine learning.

BACKGROUND: Postpartum depression is a widespread disorder, adversely affecting the well-being of mothers and their newborns. We aim to utilize machine learning for predicting risk of postpartum depression (PPD) using primary care electronic health records (EHR) data, and to evaluate the potential value of EHR-based prediction in improving the accuracy of PPD screening and in early identification of women at risk. METHODS: We analyzed EHR data of 266,544 women from the UK who gave first live birth between 2000 and 2017. We extracted a multitude of socio-demographic and medical variables and constructed a machine learning model that predicts the risk of PPD during the year following childbirth. We evaluated the model's performance using multiple validation methodologies and measured its accuracy as a stand-alone tool and as an adjunct to the standard questionnaire-based screening by Edinburgh postnatal depression scale (EPDS). RESULTS: The prevalence of PPD in the analyzed cohort was 13.4%. Combing EHR-based prediction with EPDS score increased the area under the receiver operator characteristics curve (AUC) from 0.805 to 0.844 and the sensitivity from 0.72 to 0.76, at specificity of 0.80. The AUC of the EHR-based prediction model alone varied from 0.72 to 0.74 and decreased by only 0.01-0.02 when applied as early as before the beginning of pregnancy. CONCLUSIONS: PPD risk prediction using EHR data may provide a complementary quantitative and objective tool for PPD screening, allowing earlier (pre-pregnancy) and more accurate identification of women at risk, timely interventions and potentially improved outcomes for the mother and child.

Identifying urban built environment factors in pregnancy care and maternal mental health outcomes.

BACKGROUNDS: Risk factors related to the built environment have been associated with women's mental health and preventive care. This study sought to identify built environment factors that are associated with variations in prenatal care and subsequent pregnancy-related outcomes in an urban setting. METHODS: In a retrospective observational study, we characterized the types and frequency of prenatal care events that are associated with the various built environment factors of the patients' residing neighborhoods. In comparison to women living in higher-quality built environments, we hypothesize that women who reside in lower-quality built environments experience different patterns of clinical events that may increase the risk for adverse outcomes. Using machine learning, we performed pattern detection to characterize the variability in prenatal care concerning encounter types, clinical problems, and medication prescriptions. Structural equation modeling was used to test the associations among built environment, prenatal care variation, and pregnancy outcome. The main outcome is postpartum depression (PPD) diagnosis within 1 year following childbirth. The exposures were the quality of the built environment in the patients' residing neighborhoods. Electronic health records (EHR) data of pregnant women (n = 8,949) who had live delivery at an urban academic medical center from 2015 to 2017 were included in the study. RESULTS: We discovered prenatal care patterns that were summarized into three common types. Women who experienced the prenatal care pattern with the highest rates of PPD were more likely to reside in neighborhoods with homogeneous land use, lower walkability, lower air pollutant concentration, and lower retail floor ratios after adjusting for age, neighborhood average education level, marital status, and income inequality. CONCLUSIONS: In an urban setting, multi-purpose and walkable communities were found to be associated with a lower risk of PPD. Findings may inform urban design policies and provide awareness for care providers on the association of patients' residing neighborhoods and healthy pregnancy.

Can artificial intelligence help identify elder abuse and neglect?

A health care encounter is a potentially critical opportunity to detect elder abuse and initiate intervention. Unfortunately, health care providers currently very seldom identify elder abuse. Through development of advanced data analytics techniques such as machine learning, artificial intelligence has the potential to dramatically improve elder abuse identification in health care settings.

Machine learning based adaptive optics for doughnut-shaped beam.

The doughnut-shaped beam has been widely applied in the field of super-resolution microscopic imaging, micro-nanostructure lithography, ultra-high-density storage, and laser trapping. However, how to maintain the doughnut-shaped focus inside the scattering medium becomes a challenge, due to the wavefront aberrations. Here we demonstrate a machine learning based adaptive optics method to recover the doughnut-shaped focus with high speed. In our method, the relationship between the distorted doughnut-shaped intensity point spread function and the coefficients of the first 15 Zernike modes for phase correction is established. Experimental results show that the wavefront aberration with 101,784 optical control elements can be predicted within ~17 ms even using a personal computer, and 97.5% correction accuracy can be achieved in 200 repeated tests. Besides, we successfully apply this method in the scanning microscopy theoretically. With a large number of optical control elements and fast operation speed, our method may pave the way for many important applications in bioimaging, such as deep tissue stimulated emission depletion (STED) microscopy.

Machine learning guided rapid focusing with sensor-less aberration corrections.

Non-invasive, real-time imaging and deep focus into tissue are in high demand in biomedical research. However, the aberration that is introduced by the refractive index inhomogeneity of biological tissue hinders the way forward. A rapid focusing with sensor-less aberration corrections, based on machine learning, is demonstrated in this paper. The proposed method applies the Convolutional Neural Network (CNN), which can rapidly calculate the low-order aberrations from the point spread function images with Zernike modes after training. The results show that approximately 90 percent correction accuracy can be achieved. The average mean square error of each Zernike coefficient in 200 repetitions is 0.06. Furthermore, the aberration induced by 1-mm-thick phantom samples and 300-µm-thick mouse brain slices can be efficiently compensated through loading a compensation phase on an adaptive element placed at the back-pupil plane. The phase reconstruction requires less than 0.2 s. Therefore, this method offers great potential for in vivo real-time imaging in biological science.

Association networks in a matched case-control design - Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls.

OBJECTIVE: We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls. MATERIALS AND METHODS: Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort. Differences in the networks of chronic medical conditions in newly diagnosed MDD cases treated with antidepressants, and their matched controls, were prioritized with a permutation test accounting for the false discovery rate. Sensitivity analyses for the associations between prioritized pairs of chronic medical conditions and new MDD diagnosis were performed with regression modeling. RESULTS: By comparing the association networks of chronic medical conditions in newly diagnosed depression patients and their matched controls, five pairs of such conditions were prioritized among 105 possible pairs after controlling the false discovery rate at 5%. In sensitivity analyses using regression modeling, four out of the five prioritized pairs were statistically significant for the interaction terms. CONCLUSION: Association networks in a matched case-control design can provide a high-level comparison of comorbid features after adjusting for confounding factors, thereby supplementing traditional clinical study approaches. We demonstrate the differential co-occurrence pattern of chronic medical conditions in patients with MDD and prioritize the chronic conditions that have statistically significant interactions in regression models for depression.

Cognitive workload reduction in hospital information systems : Decision support for order set optimization.

Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians' physical and cognitive workload and improve patient safety. Order sets represent time interval-clustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians' cognitive workload associated with prescribing order sets. Furthermore, we provide structural insights into the problem which lead us to a valid lower bound on the order set size. In a case study using order data on Asthma patients with moderate complexity from a major pediatric hospital, we compare the hospital's current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results confirm our lower bound and reveal that using a time interval decomposition approach substantially reduces computation times for the mathematical program, as does a K -means clustering based decomposition approach which, however, does not guarantee optimality because it violates the lower bound. The results of comparing the mathematical program with the current order set configuration in the hospital indicates that cognitive workload can be reduced by about 20.2% by allowing 1 to 5 order sets, respectively. The comparison of the K -means based decomposition with the hospital's current configuration reveals a cognitive workload reduction of about 19.5%, also by allowing 1 to 5 order sets, respectively. We finally provide a decision support system to help practitioners analyze the current order set configuration, the results of the mathematical program and the heuristic approach.

Paving the COWpath: Learning and visualizing clinical pathways from electronic health record data.

OBJECTIVE: Clinical pathways translate best available evidence into practice, indicating the most widely applicable order of treatment interventions for particular treatment goals. We propose a practice-based clinical pathway development process and a data-driven methodology for extracting common clinical pathways from electronic health record (EHR) data that is patient-centered, consistent with clinical workflow, and facilitates evidence-based care. MATERIALS AND METHODS: Visit data of 1,576 chronic kidney disease (CKD) patients who developed acute kidney injury (AKI) from 2009 to 2013 are extracted from the EHR. We model each patient's multi-dimensional clinical records into one-dimensional sequences using novel constructs designed to capture information on each visit's purpose, procedures, medications and diagnoses. Analysis and clustering on visit sequences identify distinct types of patient subgroups. Characterizing visit sequences as Markov chains, significant transitions are extracted and visualized into clinical pathways across subgroups. RESULTS: We identified 31 patient subgroups whose extracted clinical pathways provide insights on how patients' conditions and medication prescriptions may progress over time. We identify pathways that show typical disease progression, practices that are consistent with guidelines, and sustainable improvements in patients' health conditions. Visualization of pathways depicts the likelihood and direction of disease progression under varied contexts. DISCUSSION AND CONCLUSIONS: Accuracy of EHR data and diversity in patients' conditions and practice patterns are critical challenges in learning insightful practice-based clinical pathways. Learning and visualizing clinical pathways from actual practice data captured in the EHR may facilitate efficient practice review by healthcare providers and support patient engagement in shared decision making.

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness. MATERIALS AND METHODS: We used EHR data from an academic medical center (AMC) and a clinical research network database from 2014 to 2020 to evaluate the predictive performance and net benefit of the PPD risk model. We used area under the curve and sensitivity as predictive performance and conducted a decision curve analysis. In assessing model fairness, we employed metrics such as disparate impact, equal opportunity, and predictive parity with the White race being the privileged value. The model was also reviewed by multidisciplinary experts for clinical appropriateness. Lastly, we debiased the model by comparing 5 different debiasing approaches of fairness through blindness and reweighing. RESULTS: We determined the classification threshold through a performance evaluation that prioritized sensitivity and decision curve analysis. The baseline PPD model exhibited some unfairness in the AMC data but had a fair performance in the clinical research network data. We revised the model by fairness through blindness, a debiasing approach that yielded the best overall performance and fairness, while considering clinical appropriateness suggested by the expert reviewers. DISCUSSION AND CONCLUSION: The findings emphasize the need for a thorough evaluation of intervention-specific models, considering predictive performance, fairness, and appropriateness before clinical implementation.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.

OBJECTIVES: To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes). MATERIALS AND METHODS: We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome. RESULTS: Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%). DISCUSSION AND CONCLUSION: All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

Healthcare costs for legally adjudicated elder mistreatment victims in comparison to non-mistreated older adults.

BACKGROUND: Elder mistreatment (EM) is associated with adverse health outcomes and healthcare utilization patterns that differ from other older adults. However, the association of EM with healthcare costs has not been examined. Our goal was to compare healthcare costs between legally adjudicated EM victims and controls. METHODS: We used Medicare insurance claims to examine healthcare costs of EM victims in the 2 years surrounding initial mistreatment identification in comparison to matched controls. We adjusted costs using the Centers for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCC) risk score. RESULTS: We examined healthcare costs in 114 individuals who experienced EM and 410 matched controls. Total Medicare Parts A and B healthcare costs were similar between cases and controls in the 12 months prior to initial EM detection ($11,673 vs. $11,402, p = 0.92), but cases had significantly higher total healthcare costs during the 12 months after initial mistreatment identification ($15,927 vs. $10,805, p = 0.04). Adjusting for CMS-HCC scores, cases had, in the 12 months after initial EM identification, $5084 of additional total healthcare costs (95% confidence interval [$92, $10,077], p = 0.046) and $5817 of additional acute/subacute/post-acute costs (95% confidence interval [$1271, $10,362], p = 0.012) compared with controls. The significantly higher total costs and acute/sub-acute/post-acute costs among EM victims in the post-year were concentrated in the 120 days after EM detection. CONCLUSIONS: Older adults experiencing EM had substantially higher total costs during the 12 months after mistreatment identification, driven by an increase in acute/sub-acute/post-acute costs and focused on the period immediately after initial EM detection.

Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts.

BACKGROUND: SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors. METHODS: In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests. RESULTS: We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7-0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). CONCLUSIONS: This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.

Most people who develop COVID-19 make a full recovery, but some go on to develop post-acute sequelae of SARS-CoV-2 infection, commonly known as Long COVID. Up to now, we did not know why some people are affected by Long COVID whilst others are not. We conducted a study to identify risk factors for Long COVID and developed a mathematical modeling approach to predict those at risk. We find that Long COVID is associated with some factors such as experiencing severe acute COVID-19, being underweight, and having conditions including cancer or cirrhosis. Due to the wide variety of symptoms defined as Long COVID, it may be challenging to come up with a set of risk factors that can predict the whole spectrum of Long COVID. However, our approach could be used to predict a variety of Long COVID conditions.

Environmental regulation, foreign investment, and green innovation: a case study from China.

Green innovation (GI) has the dual attributes of green development and being innovation driven, and it has become an inevitable choice for solving the prisoner's dilemma of environmental protection and economic development under the action of the concept of sustainable development in the new era. This paper aims to clarify how environmental regulation (ER) can achieve a win‒win situation of GI and environmental protection by using data from prefecture-level cities in China and creating a dynamic panel model, quantile model, spatial econometric model, and panel threshold model to empirically analyze the dynamic effect and spatial effect of ER on GI as well as the nonlinear characteristics of the relationship between them and to examine the moderating effect of foreign direct investment (FDI). The results show that ER significantly promotes the development of the GI level and that FDI can play a positive moderating role. The impact has regional heterogeneity, time period heterogeneity, and resource endowment heterogeneity. After several robustness tests, the empirical conclusions are still credible. Based on the empirical conclusions, this paper makes policy suggestions on ER, foreign investment introduction, and the coordinated development of regional GI.

Construction and immunogenicity of a trypsin-independent porcine epidemic diarrhea virus variant.

Porcine epidemic diarrhea virus (PEDV) is a re-emerging enteropathogenic coronavirus that causes high mortality in neonatal piglets. The addition of trypsin plays a crucial role in the propagation of PEDV, but also increases the complexity of vaccine production and increases its cost. Previous studies have suggested that the S2' site and Y976/977 of the PEDV spike (S) protein might be the determinants of PEDV trypsin independence. In this study, to achieve a recombinant trypsin-independent PEDV strain, we used trypsin-dependent genotype 2 (G2) PEDV variant AJ1102 to generate three recombinant PEDVs with mutations in S (S2' site R894G and/or Y976H). The three recombinant PEDVs were still trypsin dependent, suggesting that the S2' site R894 and Y976 of AJ1102 S are not key sites for PEDV trypsin dependence. Therefore, we used AJ1102 and the classical trypsin-independent genotype 1 (G1) PEDV strain JS2008 to generate a recombinant PEDV carrying a chimeric S protein, and successfully obtained trypsin-independent PEDV strain rAJ1102-S2'JS2008, in which the S2 (amino acids 894-1386) domain was replaced with the corresponding JS2008 sequence. Importantly, immunization with rAJ1102-S2'JS2008 induced neutralizing antibodies against both AJ1102 and JS2008. Collectively, these results suggest that rAJ1102-S2'JS2008 is a novel vaccine candidate with significant advantages, including no trypsin requirement for viral propagation to high titers and the potential provision of protection for pigs against G1 and G2 PEDV infections.

Effects of online support and social media communities on gestational diabetes: A systematic review.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common complication in pregnancy that can lead to negative maternal and fetal outcomes. Online support interventions have been suggested as a potential tool to improve the management of GDM. OBJECTIVE: This systematic review aimed to summarize the effectiveness of social media and online support interventions for the management of GDM. METHODS: We conducted a thorough systematic search across Web of Science, Scopus, and PubMed, following PRISMA guidelines, and supplemented it with a manual search. Our results included both qualitative and quantitative research. We rigorously assessed quantitative studies for bias using ROBINS-I and RoB 2 tools, ensuring the reliability of our findings. RESULTS: We incorporated a total of 22 studies, which were comprised of ten qualitative and twelve quantitative studies. Online support interventions were found to have a positive impact on promoting self-care and improving healthcare outcomes for women with GDM. Individualized diet and exercise interventions resulted in lower odds of weight gain and GDM diagnosis, while online prenatal education increased breastfeeding rates. In addition, telemedicine options reduced the need for in-person clinical visits and improved patient satisfaction. CONCLUSIONS: Online support interventions show potential to improve outcomes in patients with GDM in this small literature review. Future research is also necessary to determine the effectiveness of different types of online interventions and identify strategies to improve engagement and the quality of the information provided through online resources.

USING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS.

The population of older adults, defined in this study as those 50 years of age or older, continues to increase every year. Substance misuse, particularly alcohol misuse, is often neglected in these individuals. To better identify older adults who might not be properly assessed for alcohol misuse, we have derived a risk assessment tool using patients from the United Kingdom Biobank (UKB), which was validated on patients in the Weill Cornell Medicine (WCM) electronic health record (EHR). The model and tooling created stratifies the risk of alcohol misuse in older adults using 10 features that are commonly found in most EHR systems. We found that the area under the receiver operating curve (AUROC) to correctly predict alcohol misuse in older adults for the UKB and WCM models were 0.84 and 0.78, respectively. We further show that of those who self-identified as having ongoing alcohol misuse in the UKB cohort, only 12.5% of these patients had any alcohol-related F.10 ICD-10 code. Extending this to the WCM cohort, we forecast that 7,838 out of 12,360 older adults with no F.10 ICD-10 code (63.4%) may be missed as having alcohol misuse in the EHR. Overall, this study importantly prioritizes the health of older adults by being able to predict alcohol misuse in an understudied population.