A network's gender composition and communication pattern predict women's leadership success.

Many leaders today do not rise through the ranks but are recruited directly out of graduate programs into leadership positions. We use a quasi-experiment and instrumental-variable regression to understand the link between students' graduate school social networks and placement into leadership positions of varying levels of authority. Our data measure students' personal characteristics and academic performance, as well as their social network information drawn from 4.5 million email correspondences among hundreds of students who were placed directly into leadership positions. After controlling for students' personal characteristics, work experience, and academic performance, we find that students' social networks strongly predict placement into leadership positions. For males, the higher a male student's centrality in the school-wide network, the higher his leadership-job placement will be. Men with network centrality in the top quartile have an expected job placement level that is 1.5 times greater than men in the bottom quartile of centrality. While centrality also predicts women's placement, high-placing women students have one thing more: an inner circle of predominantly female contacts who are connected to many nonoverlapping third-party contacts. Women with a network centrality in the top quartile and a female-dominated inner circle have an expected job placement level that is 2.5 times greater than women with low centrality and a male-dominated inner circle. Women who have networks that resemble those of high-placing men are low-placing, despite having leadership qualifications comparable to high-placing women.

Discrete Heart Rate Values or Continuous Streams? Representation, Variability, and Meaningful Use of Vital Sign Data.

Documentation and review of patient heart rate are a fundamental process across a myriad of clinical settings. While historically recorded manually, bedside monitors now provide for the automated collection of such data. Despite the availability of continuous streaming data, patients' charts continue to reflect only a subset of this information as snapshots recorded throughout a hospitalization. Over the past decade, prominent works have explored the implications of such practices and established fundamental differences in the alignment of discrete charted vitals and steaming data captured by monitoring systems. Limited work has examined the temporal properties of these differences, how they manifest, and their relation to clinical applications. The work presented in this article addresses this disparity, providing evidence that differences between charting techniques extend to measures of variability. Our results demonstrate how variability manifests with respect to temporal elements of charting timing and how it can facilitate personalized care by contextualizing deviations in magnitude. This work also highlights the utility of variability metrics with relation to clinical measures including associations to severity scores and a case study utilizing complex variability metrics derived from the complete set of monitor data.

Examining the weekend effect across ICU performance metrics.

BACKGROUND: Known colloquially as the "weekend effect," the association between weekend admissions and increased mortality within hospital settings has become a highly contested topic over the last two decades. Drawing interest from practitioners and researchers alike, a sundry of works have emerged arguing for and against the presence of the effect across various patient cohorts. However, it has become evident that simply studying population characteristics is insufficient for understanding how the effect manifests. Rather, to truly understand the effect, investigations into its underlying factors must be considered. As such, the work presented in this manuscript serves to address this consideration by moving beyond identification of patient cohorts to examining the role of ICU performance. METHODS: Employing a comprehensive, publicly available database of electronic medical records (EMR), we began by utilizing multiple logistic regression to identify and isolate a specific cohort in which the weekend effect was present. Next, we leveraged the highly detailed nature of the EMR to evaluate ICU performance using well-established ICU quality scorecards to assess differences in clinical factors among patients admitted to an ICU on the weekend versus weekday. RESULTS: Our results demonstrate the weekend effect to be most prevalent among emergency surgery patients (OR 1.53; 95% CI 1.19, 1.96), specifically those diagnosed with circulatory diseases (P<.001). Differences between weekday and weekend admissions for this cohort included a variety of clinical factors such as ventilatory support and night-time discharges. CONCLUSIONS: This work reinforces the importance of accounting for differences in clinical factors as well as patient cohorts in studies investigating the weekend effect.

Interaction between obesity and the Hypoxia Inducible Factor 3 Alpha Subunit rs3826795 polymorphism in relation with plasma alanine aminotransferase.

BACKGROUND: Hypoxia Inducible Factor 3 Alpha Subunit (HIF3A) DNA has been demonstrated to be associated with obesity in the methylation level, and it also has a Body Mass Index (BMI)-independent association with plasma alanine aminotransferase (ALT). However, the relation among obesity, plasma ALT, HIF3A polymorphism and methylation remains unclear. This study aims to identify the association between HIF3A polymorphism and plasma ALT, and further to determine whether the effect of HIF3A polymorphism on ALT could be modified by obesity or mediated by DNA methylation. METHODS: The HIF3A rs3826795 polymorphism was genotyped in a case-control study including 2030 Chinese children aged 7-18 years (705 obese cases and 1325 non-obese controls). Furthermore, the HIF3A DNA methylation of the peripheral blood was measured in 110 severely obese children and 110 age- and gender- matched normal-weight controls. RESULTS: There was no overall association between the HIF3A rs3826795 polymorphism and ALT. A significant interaction between obesity and rs3826795 in relation with ALT was found (P inter = 0.042), with rs3826795 G-allele number elevating ALT significantly only in obese children (ß' = 0.075, P = 0.037), but not in non-obese children (ß' = -0.009, P = 0.741). Additionally, a mediation effect of HIF3A methylation was found in the association between the HIF3A rs3826795 polymorphism and ALT among obese children (ß' = 0.242, P = 0.014). CONCLUSION: This is the first study to report the interaction between obesity and HIF3A gene in relation with ALT, and also to reveal a mediation effect among the HIF3A polymorphism, methylation and ALT. This study provides new evidence to the function of HIF3A gene, which would be helpful for future risk assessment and personalized treatment of liver diseases.

Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration.

Today, advances in medical informatics brought on by the increasing availability of electronic medical records (EMR) have allowed for the proliferation of data-centric tools, especially in the context of personalized healthcare. While these tools have the potential to greatly improve the quality of patient care, the effective utilization of their techniques within clinical practice may encounter two significant challenges. First, the increasing amount of electronic data generated by clinical processes can impose scalability challenges for current computational tools, requiring parallel or distributed implementations of such tools to scale. Secondly, as technology becomes increasingly intertwined in clinical workflows these tools must not only operate efficiently, but also in an interpretable manner. Failure to identity areas of uncertainty or provide appropriate context creates a potentially complex situation for both physicians and patients. This paper will present a case study investigating the issues associated with first scaling a disease prediction algorithm to accommodate dataset sizes expected in large medical practices. It will then provide an analysis on the diagnoses predictions, attempting to provide contextual information to convey the certainty of the results to a physician. Finally it will investigate latent demographic features of the patient's themselves, which may have an impact on the accuracy of the diagnosis predictions.

Corrigendum: A community focused approach toward making healthy and affordable daily diet recommendations.

[This corrects the article DOI: 10.3389/fdata.2023.1086212.].

Bringing big data to personalized healthcare: a patient-centered framework.

Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of personal health and healthcare-related data. Imagine visiting your physician's office with a list of concerns and questions. What if you could walk out the office with a personalized assessment of your health? What if you could have personalized disease management and wellness plan? These are the goals and vision of the work discussed in this paper. The timing is right for such a research direction--given the changes in health care, reimbursement, reform, meaningful use of electronic health care data, and patient-centered outcome mandate. We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates.

Storing and using health data in a virtual private cloud.

Electronic health records are being adopted at a rapid rate due to increased funding from the US federal government. Health data provide the opportunity to identify possible improvements in health care delivery by applying data mining and statistical methods to the data and will also enable a wide variety of new applications that will be meaningful to patients and medical professionals. Researchers are often granted access to health care data to assist in the data mining process, but HIPAA regulations mandate comprehensive safeguards to protect the data. Often universities (and presumably other research organizations) have an enterprise information technology infrastructure and a research infrastructure. Unfortunately, both of these infrastructures are generally not appropriate for sensitive research data such as HIPAA, as they require special accommodations on the part of the enterprise information technology (or increased security on the part of the research computing environment). Cloud computing, which is a concept that allows organizations to build complex infrastructures on leased resources, is rapidly evolving to the point that it is possible to build sophisticated network architectures with advanced security capabilities. We present a prototype infrastructure in Amazon's Virtual Private Cloud to allow researchers and practitioners to utilize the data in a HIPAA-compliant environment.

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.

Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have further magnified the importance of the imbalanced data problem, especially when learning from images. Therefore, there is a need for an oversampling method that is specifically tailored to deep learning models, can work on raw images while preserving their properties, and is capable of generating high-quality, artificial images that can enhance minority classes and balance the training set. We propose Deep synthetic minority oversampling technique (SMOTE), a novel oversampling algorithm for deep learning models that leverages the properties of the successful SMOTE algorithm. It is simple, yet effective in its design. It consists of three major components: 1) an encoder/decoder framework; 2) SMOTE-based oversampling; and 3) a dedicated loss function that is enhanced with a penalty term. An important advantage of DeepSMOTE over generative adversarial network (GAN)-based oversampling is that DeepSMOTE does not require a discriminator, and it generates high-quality artificial images that are both information-rich and suitable for visual inspection. DeepSMOTE code is publicly available at https://github.com/dd1github/DeepSMOTE.

A community focused approach toward making healthy and affordable daily diet recommendations.

Introduction: Maintaining an affordable and nutritious diet can be challenging, especially for those living under the conditions of poverty. To fulfill a healthy diet, consumers must make difficult decisions within a complicated food landscape. Decisions must factor information on health and budget constraints, the food supply and pricing options at local grocery stores, and nutrition and portion guidelines provided by government services. Information to support food choice decisions is often inconsistent and challenging to find, making it difficult for consumers to make informed, optimal decisions. This is especially true for low-income and Supplemental Nutrition Assistance Program (SNAP) households which have additional time and cost constraints that impact their food purchases and ultimately leave them more susceptible to malnutrition and obesity. The goal of this paper is to demonstrate how the integration of data from local grocery stores and federal government databases can be used to assist specific communities in meeting their unique health and budget challenges. Methods: We discuss many of the challenges of integrating multiple data sources, such as inconsistent data availability and misleading nutrition labels. We conduct a case study using linear programming to identify a healthy meal plan that stays within a limited SNAP budget and also adheres to the Dietary Guidelines for Americans. Finally, we explore the main drivers of cost of local food products with emphasis on the nutrients determined by the USDA as areas of focus: added sugars, saturated fat, and sodium. Results and discussion: Our case study results suggest that such an optimization model can be used to facilitate food purchasing decisions within a given community. By focusing on the community level, our results will inform future work navigating the complex networks of food information to build global recommendation systems.

SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance.

Comprehensively evaluating and comparing researchers' academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this article, we present an interactive visualization framework, SD 2, to enable flexible data partition and composition to support various analysis requirements within a single system. SD 2 features the hierarchical histogram, a novel visual representation for flexibly slicing and dicing the data, allowing different aspects of scholarly performance to be studied and compared. We also leverage the state-of-the-art set visualization technique to select individual researchers or combine multiple scholars for comprehensive visual comparison. We conduct multiple rounds of expert evaluation to study the effectiveness and usability of SD 2 and revise the design and system implementation accordingly. The effectiveness of SD 2 is demonstrated via multiple usage scenarios with each aiming to answer a specific, commonly raised question.

Deep Multimodal Complementarity Learning.

Complementarity plays a significant role in the synergistic effect created by different components of a complex data object. Complementarity learning on multimodal data has fundamental challenges of representation learning because the complementarity exists along with multiple modalities and one or multiple items of each modality. Also, an appropriate metric is needed for measuring the complementarity in the representation space. Existing methods that rely on similarity-based metrics cannot adequately capture the complementarity. In this work, we propose a novel deep architecture for systematically learning the complementarity of components from multimodal multi-item data. The proposed model consists of three major modules: 1) unimodal aggregation for extracting the intramodal complementarity; 2) cross-modal fusion for extracting the intermodal complementarity at the modality level; and 3) interactive aggregation for extracting the intermodal complementarity at the item level. To quantify complementarity, we utilize the TUBE distance metric to measure the difference between the composited data object and its label in the representation space. Experiments on three real datasets show that our model outperforms the state-of-the-art by +6.8% of mean reciprocal rank (MRR) on object classification and +3.0% of MRR on hold-out item prediction. Qualitative analyses reveal that complementarity is significantly different from similarity.

On the use of real-world datasets for reaction yield prediction.

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.

Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App.

Mobile health (mHealth) technologies offer an opportunity to enable the care and support of community-dwelling older adults, however, research examining the use of mHealth in delivering quality of life (QoL) improvements in the older population is limited. We developed a tablet application (eSeniorCare) based on the Successful Aging framework and investigated its feasibility among older adults with low socioeconomic status. Twenty five participants (females = 14, mean age = 65 years) used the app to set and track medication intake reminders and health goals, and to play selected casual mobile games for 24 weeks. The Older person QoL and Short Health (SF12v2) surveys were administered before and after the study. The Wilcoxon rank tests were used to determine differences from baseline, and thematic analysis was used to analyze post-study interview data. The improvements in health-related QoL (HRQoL) scores were statistically significant (V=41.5, p=0.005856) across all participants. The frequent eSeniorCare users experienced statistically significant improvements in their physical health (V=13, p=0.04546) and HRQoL (V=7.5, p=0.0050307) scores. Participants reported that the eSeniorCare app motivated timely medication intake and health goals achievement, whereas tablet games promoted mental stimulation. Participants were willing to use mobile apps to self-manage their medications (70%) and adopt healthy activities (72%), while 92% wanted to recommend eSeniorCare to a friend. This study shows the feasibility and possible impact of an mHealth tool on the health-related QoL in older adults with a low socioeconomic status. mHealth support tools and future research to determine their effects are warranted for this population.

Predicting terrorist attacks in the United States using localized news data.

Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. To address this threat, we propose a novel feature representation method and evaluate machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model (a Random Forest aided by a novel variable-length moving average method) achieved area under the receiver operating characteristic (AUROC) of ≥ 0.667 (statistically significant w.r.t. random guessing with p ≤ .0001) on four of the five states that were impacted most by terrorism between 2015 and 2018. These results demonstrate that treating terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach-especially when historical events are sparse and dissimilar-and that large-scale news data contains information that is useful for terrorism prediction. Our analysis also suggests that predictive models should be localized (i.e., state models should be independently designed, trained, and evaluated) and that the characteristics of individual attacks (e.g., responsible group or weapon type) were not correlated with prediction success. These contributions provide a foundation for the use of machine learning in efforts against terrorism in the United States and beyond.

Symptomatic, Presymptomatic, and Asymptomatic Transmission of SARS-CoV-2 in a University Student Population, August-November 2020.

OBJECTIVES: The impact and risk of SARS-CoV-2 transmission from asymptomatic and presymptomatic hosts remains an open question. This study measured the secondary attack rates (SARs) and relative risk (RR) of SARS-CoV-2 transmission from asymptomatic and presymptomatic index cases as compared with symptomatic index cases. METHODS: We used COVID-19 test results, daily health check reports, and contact tracing data to measure SARs and corresponding RRs among close contacts of index cases in a cohort of 12 960 young adults at the University of Notre Dame in Indiana for 103 days, from August 10 to November 20, 2020. Further analysis included Fisher exact tests to determine the association between symptoms and COVID-19 infection and z tests to determine statistical differences between SARs. RESULTS: Asymptomatic rates of transmission of SARS-CoV-2 were higher (SAR = 0.19; 95% CI, 0.14-0.24) than was estimated in prior studies, producing an RR of 0.75 (95% CI, 0.54-1.07) when compared with symptomatic transmission. In addition, the transmission rate associated with presymptomatic cases (SAR = 0.25; 95% CI, 0.21-0.30) was approximately the same as that for symptomatic cases (SAR = 0.25; 95% CI, 0.19-0.31). Furthermore, different symptoms were associated with different transmission rates. CONCLUSIONS: Asymptomatic and presymptomatic hosts of SARS-CoV-2 are a risk for community spread of COVID-19, especially with new variants emerging. Moreover, typical symptom checks may easily miss people who are asymptomatic or presymptomatic but still infectious. Our study results may be used as a guide to analyze the spread of SARS-CoV-2 variants and help inform appropriate public health measures as they relate to asymptomatic and presymptomatic cases.

Data-driven testing program improves detection of COVID-19 cases and reduces community transmission.

COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machine learning models to predict which students were at elevated risk and should be tested. The program produced a positivity rate of 0.53% (95% CI 0.34-0.77%) from 20,862 tests, with 1.49% (95% CI 1.15-1.89%) of students testing positive within five days of the initial test-a significant increase from the general surveillance baseline, which produced a positivity rate of 0.37% (95% CI 0.28-0.47%) with 0.67% (95% CI 0.55-0.81%) testing positive within five days. Close contacts who were predicted by the data-driven models were tested much more quickly on average (0.94 days from reported exposure; 95% CI 0.78-1.11) than those who were manually contact traced (1.92 days; 95% CI 1.81-2.02). We further discuss how other universities, business, and organizations could adopt similar strategies to help quickly identify positive cases and reduce community transmission.

Predictors of Septic Shock or Bacteremia in Children Experiencing Febrile Neutropenia Post-Chemotherapy.

BACKGROUND: Febrile neutropenia (FN) is an early indicator of infection in oncology patients post-chemotherapy. We aimed to determine clinical predictors of septic shock and/or bacteremia in pediatric cancer patients experiencing FN and to create a model that classifies patients as low-risk for these outcomes. METHODS: This is a retrospective analysis with clinical data of a cohort of pediatric oncology patients admitted during July 2015 to September 2017 with FN. One FN episode per patient was randomly selected. Statistical analyses include distribution analysis, hypothesis testing, and multivariate logistic regression to determine clinical feature association with outcomes. RESULTS: A total of 865 episodes of FN occurred in 429 subjects. In the 404 sampled episodes that were analyzed, 20.8% experienced outcomes of septic shock and/or bacteremia. Gram-negative bacteria count for 70% of bacteremias. Features with statistically significant influence in predicting these outcomes were hematological malignancy (P < .001), cancer relapse (P = .011), platelet count (P = .004), and age (P = .023). The multivariate logistic regression model achieves AUROC = 0.66 (95% CI 0.56-0.76). The optimal classification threshold achieves sensitivity = 0.96, specificity = 0.33, PPV = 0.40, and NPV = 0.95. CONCLUSIONS: This model, based on simple clinical variables, can be used to identify patients at low-risk of septic shock and/or bacteremia. The model's NPV of 95% satisfies the priority to avoid discharging patients at high-risk for adverse infection outcomes. The model will require further validation on a prospective population.

Recipe Recommendation With Hierarchical Graph Attention Network.

Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches, the relational information among users, recipes, and food items is less explored. In this paper, we leverage the relational information into recipe recommendation and propose a graph learning approach to solve it. In particular, we propose HGAT, a novel hierarchical graph attention network for recipe recommendation. The proposed model can capture user history behavior, recipe content, and relational information through several neural network modules, including type-specific transformation, node-level attention, and relation-level attention. We further introduce a ranking-based objective function to optimize the model. Thorough experiments demonstrate that HGAT outperforms numerous baseline methods.

Examining Response to Negative Life Events Through Fitness Tracker Data.

Negative life events, such as the death of a loved one, are an unavoidable part of life. These events can be overwhelmingly stressful and may lead to the development of mental health disorders. To mitigate these adverse developments, prior literature has utilized measures of psychological responses to negative life events to better understand their effects on mental health. However, psychological changes represent only one aspect of an individual's potential response. We posit measuring additional dimensions of health, such as physical health, may also be beneficial, as physical health itself may be affected by negative life events and measuring its response could provide context to changes in mental health. Therefore, the primary aim of this work was to quantify how an individual's physical health changes in response to negative life events by testing for deviations in their physiological and behavioral state (PB-state). After capturing post-event, PB-state responses, our second aim sought to contextualize changes within known factors of psychological response to negative life events, namely coping strategies. To do so, we utilized a cohort of professionals across the United States monitored for 1 year and who experienced a negative life event while under observation. Garmin Vivosmart-3 devices provided a multidimensional representation of one's PB-state by collecting measures of resting heart rate, physical activity, and sleep. To test for deviations in PB-state following negative life events, One-Class Support Vector Machines were trained on a window of time prior to the event, which established a PB-state baseline. The model then evaluated participant's PB-state on the day of the life event and each day that followed, assigning each day a level of deviance relative to the participant's baseline. Resulting response curves were then examined in association with the use of various coping strategies using Bayesian gamma-hurdle regression models. The results from our objectives suggest that physical determinants of health also deviate in response to negative life events and that these deviations can be mitigated through different coping strategies. Taken together, these observations stress the need to examine physical determinants of health alongside psychological determinants when investigating the effects of negative life events.