Dataset Of Recordings Research Articles

Safety of abdominal paracentesis in hospitalised patients receiving uninterrupted therapeutic or prophylactic anticoagulants

AbstractBackgroundAbdominal paracentesis is a frequently performed procedure in hospitalised patients with ascites. Concurrently, most hospitalised adult patients receive pharmacologic anticoagulation, either for therapeutic purposes or prophylactically to prevent venous thromboembolism. Despite this, minimal evidence exists to guide management of anticoagulant therapy pre‐ and post‐paracentesis.AimsThe authors aimed to investigate the safety of abdominal paracentesis in hospitalised patients receiving therapeutic or prophylactic anticoagulation, including in patients for whom these medications were withheld periprocedurally.MethodsTriNetX, an electronic health record data set, was queried to identify patients between the ages of 18 and 80 years who received an abdominal paracentesis while hospitalised at the authors' institution between September 2017 and June 2022. Patients receiving prophylactic anticoagulation (137), therapeutic anticoagulation (74) and no anticoagulation because of coagulopathy or thrombocytopenia (15) were compared. Rates of withholding anticoagulation, performing service, pre‐ and post‐paracentesis haemoglobin, bleeding complications, thrombotic complications and need for red blood cell transfusion were analysed.ResultsProcedure‐related bleeding complications occurred in two (1.4%) patients in the prophylactic group and 0 (0%) patients in the therapeutic group (P = 0.54). No thrombotic complications occurred. Rates of red blood cell transfusions post‐paracentesis were similar between groups. Analysis of secondary end‐points identified significant differences in rates of withholding anticoagulation and mean change in haemoglobin between performing services.ConclusionPerformance of abdominal paracentesis in patients receiving therapeutic or prophylactic anticoagulation appears to be safe regardless of whether anticoagulation was interrupted periprocedurally, with low rates of bleeding complications, thrombotic complications or need for red blood cell transfusions post‐paracentesis.

Quantitatively assessing the impact of the quality of SNOMED CT subtype hierarchy on cohort queries

Abstract Objective SNOMED CT provides a standardized terminology for clinical concepts, allowing cohort queries over heterogeneous clinical data including Electronic Health Records (EHRs). While it is intuitive that missing and inaccurate subtype (or is-a) relations in SNOMED CT reduce the recall and precision of cohort queries, the extent of these impacts has not been formally assessed. This study fills this gap by developing quantitative metrics to measure these impacts and performing statistical analysis on their significance. Material and Methods We used the Optum de-identified COVID-19 Electronic Health Record dataset. We defined micro-averaged and macro-averaged recall and precision metrics to assess the impact of missing and inaccurate is-a relations on cohort queries. Both practical and simulated analyses were performed. Practical analyses involved 407 missing and 48 inaccurate is-a relations confirmed by domain experts, with statistical testing using Wilcoxon signed-rank tests. Simulated analyses used two random sets of 400 is-a relations to simulate missing and inaccurate is-a relations. Results Wilcoxon signed-rank tests from both practical and simulated analyses (P-values < .001) showed that missing is-a relations significantly reduced the micro- and macro-averaged recall, and inaccurate is-a relations significantly reduced the micro- and macro-averaged precision. Discussion The introduced impact metrics can assist SNOMED CT maintainers in prioritizing critical hierarchical defects for quality enhancement. These metrics are generally applicable for assessing the quality impact of a terminology’s subtype hierarchy on its cohort query applications. Conclusion Our results indicate a significant impact of missing and inaccurate is-a relations in SNOMED CT on the recall and precision of cohort queries. Our work highlights the importance of high-quality terminology hierarchy for cohort queries over EHR data and provides valuable insights for prioritizing quality improvements of SNOMED CT's hierarchy.

Discover overlooked complications after preeclampsia from three real-world medical record datasets of over 100,000 pregnancies.

Structured Abstract Importance Preeclampsia poses a significant threat to women's long-term health. However, what diseases are affected and at what level they are affected by PE needs a thorough investigation. Objective To conduct the first large-scale, non-hypothesis-driven study using EHR data from multiple medical centers to comprehensively explore adverse health outcomes after preeclampsia Design: Retrospective multi-cohort case-control study Participants: We analyzed 3,592 preeclampsia patients and 23,040 non-preeclampsia controls from the University of Michigan Healthcare System. We externally validated the findings using UK Biobank data (443 cases, 14,870 controls) and Cedar Sinai data(2755 cases, 60,305 controls). Main outcomes: We showed that six complications are significantly affected by PE. We demonstrate the effect of race as well as preeclampsia severity on these complications. Results PE significantly increases the risk of later hypertension, uncomplicated and complicated diabetes, renal failure and obesity, after careful confounder adjustment. We also identified that hypothyroidism risks are significantly reduced in PE patients, particularly among African Americans. Severe PE affects hypertension, renal failure, uncomplicated diabetes and obesity more than mild PE, as expected. Caucasians are affected more negatively than African Americans by PE on future hypertension, uncomplicated and complicated diabetes and obesity. Conclusion This study fills a gap in the comprehensive assessment of preeclampsia's long-term effects using large-scale EHR data and rigorous statistical methods. Our findings emphasize the need for extended monitoring and tailored interventions for women with a history of preeclampsia, by considering pre-existing conditions, preeclampsia severity, and racial differences.

Who transitions to bipolar disorder? A comparison of major depressive disorder, anxiety, and ADHD

BackgroundDiagnostic delays in Bipolar Disorder (BD) are common and can contribute to worse outcomes. While most studies focus on depression as a primary precursor, both anxiety and attention deficit disorders are also frequent initial diagnoses. In the current study, we utilized a large, diverse electronic health record (EHR) dataset to quantify the rates and correlates of conversion to BD from these major precursor diagnoses. MethodsOur study analyzed a comprehensive ten-year EHR dataset from Johns Hopkins Medicine, a diverse urban medical center, to assess and compare the rates and correlates of conversion to BD from Major Depressive Disorder (MDD), anxiety disorders, and ADHD. Risk factors for transition were assessed as time-varying variables in proportional hazards models. ResultsOf the 21,341 patients initially included, 1232 later transitioned to a diagnosis of BD. Adjusted-one-year conversion rates for patients with MDD, anxiety disorders, and ADHD were 4.2 %, 3.4 %, and 4.0 %, respectively, with ten-year rates at 11.4 %, 9.4 %, and 10.9 %, respectively. Age (19–29 years), treatment setting (emergency and inpatient), and psychotropic medications were associated with conversion to BD across all precursor diagnoses. Severe and psychotic forms of MDD were among the strongest risk factors for transitioning to BD. Although transition rates were lower in children, risk factors were similar, with the exception of ADHD, which showed a stronger association with BD conversion in adults. ConclusionsThe highest risk of transitioning to BD was observed in patients initially diagnosed with MDD, though significant risk was also noted in those with initial diagnoses of anxiety disorders and adult ADHD.

Integrating the cellular vortex method with remote sensing and geographical information systems in the modelling of coastal flooding around Niger Delta

Coastal areas are increasingly vulnerable to flooding, necessitating accurate simulation methods to understand flood dynamics and their potential impacts. This study employed a Lagrangian framework integrating the cellular vortex method with remote sensing and GIS to simulate flood height distribution in a coastal region. Leveraging climatic and remotely sensed data, alongside ArcMap 10.6.1 for map processing, the research estimated flood magnitude and frequency using the L-moment approach, applied to a forty-year tidal record dataset. Essential input parameters, such as the roughness coefficient and curve number, were derived from land use and land cover characteristics. Additionally, river flow velocity was observed at 0.12m/s, with measured wind speed and direction recorded at 4m/s in the northwest direction. Notably, analysis of the initial flood height distribution map revealed a significant expansion of wetland areas, attributed to observed land use changes between May 2002 and July 2005. Projections for flood height distribution in 2025 and 2050 highlighted the emergence of tidal floods, emphasizing the critical role of considering future climate and land use scenarios in flood dynamics assessment. This research contributes to advancing understanding of flood modeling techniques and underscores the urgency of adaptive measures to mitigate the potential impacts of coastal flooding.

A multiple session dataset of NIRS recordings from stroke patients controlling brain–computer interface

This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real–time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near–infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.

Improved modelling of low-pressure rotor speed in commercial turbofan engines: A comprehensive analysis of machine learning approaches

Accurate engine thrust modelling offers notable opportunities for reducing fuel consumption, mitigating emission effects, managing air traffic, and optimizing the preliminary design of aircraft. This study focuses on the development of machine learning models to predict the low-pressure rotor speed (N1) parameter, which is closely related to the thrust of turbofan engines, for commercial aircraft during the cruise phase. The analysis utilizes a Flight Data Recorder (FDR) dataset from 1086 actual flights involving twin-engine, narrow-body, short-to-medium haul commercial aircraft equipped with CFM56-7B turbofan engines. To achieve accurate N1 parameter predictions, the study employs machine learning models, including Deep Learning (DL), Random Forest (RF), Gradient Boosted Machines (GBM), and Artificial Neural Networks (ANN). These models are evaluated using statistically significant indicators, demonstrating that machine learning models yield highly accurate results. Among the models tested, the DL model provides the most precise estimates of the N1 parameter. This study not only advances the understanding of engine thrust modelling through machine learning but also provides practical insights for the aerospace industry. The findings underline the potential of machine learning techniques in delivering superior prediction accuracy, which can be integrated into real-time flight management systems, ultimately contributing to more sustainable and efficient aviation operations.

GLMI: An Efficient Spatiotemporal Index Leveraging Geohash and Piecewise Linear Models for Optimized Query Performance

Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency.

SUBSCRIPTION FRAUD DETECTION USING AUTOENCODER IN CASE OF ETHIOTELECOM

Nowadays, telecom services are becoming an essential communication and business facilitators. However, the development of telecom services motivates fraudsters for illegal use. Hence, telecom fraud becomes a serious challenge in telecommunication sector by losing telecom revenue. It also results in poor quality of services for their customers. Telecom data, SMS and voice call are not free from security issues. Despite SMS, and USSD services becomes good options over installable mobile based or web based applications due to lesser cost and real time support, the methods provide fixed amount requirements which is not dynamic way to address the need of these services subscribers. The other challenge of these services is that; the services are not safe from frauds as such, especially in GSM switches. Subscription fraud is one type of fraud in today’s telecom business which is a common telecom fraud. The need of fraudsters is to make money illegally or getting telecom services with the intention of not to pay for the service they used. The prime objective of this study is to build a predictive model using deep auto-encoder to detect subscription fraud in case of Ethiotelecom. The performance of fraud detection model is 98.95% validation accuracy on ethiotelecom call detail record dataset using deep neural network autoencoder and CNN-LSTM autoencder algorithms at threshold of 0.0103 and 0173 respectively.

Deep Learning Model to Analyze Psychological Effects on Gamers

The rapid growth of the gaming industry has sparked interest in understanding the psychological effects of games on gamers. This paper explores using deep learning models to analyze the psychological effects experienced by gamers. With video games' increasing popularity and influence, understanding their impact on human psychology is paramount. This research aims to develop a deep learning model that can detect the state of human facial expressions and classify various psychological states based on gameplay data. The proposed deep learning model utilizes Convolutional neural networks to extract meaningful features and patterns from gameplay data. The model is trained on a diverse dataset of gameplay recordings, capturing a wide range of game genres, player demographics, and psychological experiences. The evaluation and validation of the model are performed using metrics, including accuracy, precision, recall, and F1 score. The findings reveal that the deep learning model is effective in detecting and classifying psychological states experienced by gamers. The model successfully captures changes in human facial expressions, such as sadness, happiness, fear, anger, surprise, and disgust, providing valuable insights into the psychological impact of gaming. The results highlight the importance of considering individual differences and contextual factors when examining the influence of games on human psychology. We can advance our knowledge and promote healthy and meaningful gaming experiences by continuing to investigate this complex relationship

Multi-Class Detection of Neurodegenerative Diseases from EEG Signals Using Lightweight LSTM Neural Networks.

Neurodegenerative diseases severely impact the life of millions of patients worldwide, and their occurrence is more and more increasing proportionally to longer life expectancy. Electroencephalography has become an important diagnostic tool for these diseases, due to its relatively simple procedure, but it requires analyzing a large number of data, often carrying a small fraction of informative content. For this reason, machine learning tools have gained a considerable relevance as an aid to classify potential signs of a specific disease, especially in its early stages, when treatments can be more effective. In this work, long short-term memory-based neural networks with different numbers of units were properly designed and trained after accurate data pre-processing, in order to perform a multi-class detection. To this end, a custom dataset of EEG recordings from subjects affected by five neurodegenerative diseases (Alzheimer's disease, frontotemporal dementia, dementia with Lewy bodies, progressive supranuclear palsy, and vascular dementia) was acquired. Experimental results show that an accuracy up to 98% was achieved with data belonging to different classes of disease, up to six including the control group, while not requiring particularly heavy computational resources.

An AutoEncoder enhanced light gradient boosting machine method for credit card fraud detection

Online financial transactions bring convenience to people’s lives, but also present vulnerabilities for criminals to embezzle users’ accounts and trick users into credit card fraud. Although machine learning methods have been adopted to detect anomalous transactions, it’s hard for a single machine learning method to achieve satisfying results with the increasing scale and dimensionality of financial datasets. In addition, for anomaly detection of financial data, there is an obvious imbalance between normal records and abnormal. In this situation, the experimental results cannot be objectively evaluated only by the traditional metrics, such as precision, recall, and accuracy. This paper proposes an AutoEncoder enhanced LightGBM method for credit card detection. The method inherits the advantages of each component, using an AutoEncoder for feature reconstruction on the dataset, and integrating the LightGBM algorithm for improving the GBDT (Gradient Boosting Decison Tree) to detect abnormal data more accurately and efficiently. Besides the traditional evaluation metrics, F-measure, area under curve (AUC), Matthew’s correlation coefficient (MCC), and balanced classification rate (BCR) are also adopted as the evaluation metrics. Two financial datasets were used to validate the performance and robustness of the proposed model. Results obtained from the credit card fraud dataset containing 31 features indicate that our model significantly outperforms other models with a recall of 94.85%, representing a 10.70% improvement compared to the best detection performance model with a recall of only 86%. Additionally, our model’s BCR score is also significantly better than other models, with a BCR score of 97%, as opposed to the best detection performance model’s BCR score of 92%, representing a 5% improvement by our model. Various sampling methods and model combinations were considered in this study. It was found that the SMOTE algorithm combined with the proposed model produced the best results, with an AUC value of 96.83% and an F-measure score of 80.27%. The Santander bank transaction record dataset is a high dimensional large dataset containing 200 features. Experimental results on this dataset reveal that compared to other models, our model significantly improved recall and F-measure results, raising the recall to 94.14% and the F-measure score by 11.51%, surpassing the second-best-performing model. Overall, these findings demonstrate the robustness and superiority of our model in detecting fraudulent transactions and highlight the effectiveness of the SMOTE algorithm in combination with the proposed model.

Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset.

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either "explosion", "ambient", or "other" with true positive rates (recall) greater than 96% for all three categories.

NPM1 Minimal Residual Disease Detection at Any Time by Clinical Next Generation Sequencing Predicts Poor Survival in Acute Myeloid Leukemia

Abstract Introduction/Objective Acute Myeloid Leukemia (AML) is a deadly disease that accounts for 80% of adult leukemia cases and carries a 5-year survival rate of only 31.7%. Historically, prognosis hinged heavily on cytogenetic stratification, but more recently minimal residual disease (MRD) has emerged as a crucial prognostic marker. Flow cytometry, PCR, and NGS can all be employed for MRD detection, but each carries its own limitations. Though technically challenging, NGS has the advantage being able to simultaneously assay multiple mutations, thus providing for broader applicability. NPM1 is the most prevalently mutated gene in AML (25-35%). In this study, one of the largest of its kind, we investigate the feasibility and impact of NPM1 MRD detection by clinical NGS. Methods/Case Report IRB approval was obtained. We reviewed the medical record and clinical laboratory data sets from AML patients treated at Moffitt Cancer Center who had NPM1 status assessed by clinical NGS, Testing was performed at LabPMM (San Diego, CA). DNA was extracted, and NPM1 exon 12 was amplified. PCR products were sequenced on an Illumina MiSeq. The clinically reportable assay sensitivity was 5 x 10-5 though lower levels were detected on review of raw data. Kaplan-Meier analysis was conducted for overall survival assessment. Since patients often had multiple tests, we divide patients into those with NPM1+ at any time and NPM1 always negative. Results (if a Case Study enter NA) There was a total of 140 patients. Overall, 346 tests were conducted, with an average of 2.5 tests/patient. There were 85 patients (60%) who underwent serial testing. MRD+ patients (n=80) had a mean age of 66 years, while MRD- patients (n=60) had a mean age of 58 years. MRD+ patients exhibited a mean overall survival of 29.3 months versus 43.6 months for MRD- patients (p=0.005.) Conclusion Through our study encompassing a large patient cohort of 140 patients and nearly 350 testing data points, we confirm the feasibility and clinical utility of NPM1 MRD evaluation by NGS. The significantly poorer survival in patients who were MRD+ at any time during their disease provides a scientific rationale for serial NGS testing to enable risk stratification of NPM1+ AML patients.

An Effective DNA Methylation Biomarker Screening Mechanism for Amyotrophic Lateral Sclerosis (ALS) Based on Comorbidities and Gene Function Analysis.

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved.

B-137 Sepsis risk prediction using machine learning (ML) and routine blood markers up to 1 week before emergency admission

Abstract Background Sepsis is a major global health concern, responsible for approximately 50 million cases and 11 million deaths annually, representing 20% of global mortality. It is the leading cause of hospital readmissions and deaths, with associated costs exceeding $38 billion per year. Alarmingly, 80% of sepsis cases occur outside the hospital, and 70% of patients visit an outpatient facility within a week of admission. Early diagnosis and appropriate treatment could prevent 80% of sepsis-related deaths. Our study demonstrates that machine learning models hold significant promise in identifying sepsis risks using routine blood tests up to 1 week before admission. Methods For our study, we utilized a dataset of over 25,000 patient records, including both sepsis and non-sepsis cases, sourced from MIMIC-IV (version 2.2), a comprehensive electronic health record dataset from Beth Israel Deaconess Medical Center, Boston, MA. We employed a gradient boosted model with 300 trees, a maximal depth of 30 layers, and gain ratio as the criterion for attribute selection. The model's performance was evaluated using 10-fold cross-validation, demonstrating optimal performance. Input parameters included age, gender, and results of routine blood markers such as complete blood counts, differential counts, comprehensive metabolic panels, and lipid panels recorded up to 1 week before sepsis diagnosis. Results Our model achieved remarkable predictive accuracy for sepsis risk, with an area under the receiver operating characteristic curve (AUC) of 0.99 and accuracy of 0.99. Notably, the model achieved 1.0 sensitivity, 0.99 specificity, 0.99 positive predictive value, 1.0 negative predictive value, 0.99 F measure, and 0.99 Youden index. Calcium, protein, liver enzymes, hematocrit, white blood cells, and cholesterol were identified as key contributors to sepsis risk prediction. Importantly, prediction accuracy remained consistent up to 1 week prior to diagnosis. Conclusions In conclusion, dysregulated intracellular calcium handling during sepsis is associated with an intensified inflammatory response, cellular death, and subsequent organ dysfunction. Low serum protein levels, particularly albumin, are linked to poor outcomes in sepsis. Recent studies have identified liver dysfunction as an early event in sepsis, underscoring its significance in the disease process. Elevated bilirubin levels are associated with an increased risk of mortality. Low hematocrit (HCT) levels are linked to higher 30-day mortality and serve as an independent prognostic biomarker in sepsis. Monocytes play a significant role in sepsis pathophysiology, undergoing various changes including preserved phagocytic activity, increased reactive oxygen species and nitric oxide generation, and decreased production of inflammatory cytokines such as IL-6 and TNF-alpha. Septic patients often exhibit lymphopenia, affecting CD4 and CD8 T cells, B cells, and natural killer (NK) cells. Hypocholesterolemia is common in sepsis and serves as an important early prognostic factor, with low plasma concentrations associated with poor outcomes. Since every hour of delayed treatment increases the risk of death by 4%-9%, changes in routine blood markers detected by machine learning models can provide early indications of impending sepsis up to 1 week before diagnosis.

Risk Factors for Empiric Treatment Failure in US Female Outpatients with Uncomplicated Urinary Tract Infection: an Observational Study.

Treatment failure (TF) in uncomplicated urinary tract infection (uUTI) increases disease burden and risk of antimicrobial resistance. Identification of risk factors for TF could inform empiric treatment decisions and reduce suboptimal outcomes. To evaluate the incidence of TF to empirically prescribed oral antibiotics and identify risk factors for TF in females with uUTI in the United States (US). This retrospective cohort study used Optum's de-identified Electronic Health Record dataset (January 2017-September 2022). Eligible female patients aged ≥ 12years had ≥ 1 diagnosis of urinary tract infection (UTI) in an outpatient ambulatory/emergency department (ED) setting, ≥ 1 empiric oral antibiotic prescription, and no evidence of complicated UTI (cUTI). TF was defined as having a new/repeat oral antibiotic prescription, IV antibiotic administration or acute UTI diagnosis ≤ 28days following initial empiric oral antibiotic prescription​. Risk factors of TF were selected using LASSO and reported using adjusted risk ratios (aRR) and 95% CIs. Of 376,004 patients with uUTI, 62,873 (16.7%) experienced TF. Incidence of TF was highest in patients with history of antibiotic TF (33.9%) or fosfomycin prescription (30.1%). Significant risk factors of TF included ≥ 3 prior antibiotic prescriptions (aRR [95% CI]: 1.60 [1.56-1.64]); fosfomycin prescription (1.60 [1.38-1.86]); uUTI diagnosis in ED (1.49 [1.46-1.52]), Southern US residence (1.37 [1.35-1.40]), age ≥ 75years (1.35 [1.29-1.41]), recurrent UTI (1.12 [1.10-1.14]) and obesity (1.06 [1.04-1.08]). Incidence of TF to empirically prescribed oral antibiotics for uUTI is considerable. Prior infections requiring antibiotic prescription and location of care are key risk factors for TF in female outpatients with uUTI. Knowledge of these TF risk factors can inform shared-decision making and supplement existing guidance on uUTI treatment.

RELAX-Jr: An Automated Pre-Processing Pipeline for Developmental EEG Recordings.

Automated EEG pre-processing pipelines provide several key advantages over traditional manual data cleaning approaches; primarily, they are less time-intensive and remove potential experimenter error/bias. Automated pipelines also require fewer technical expertise as they remove the need for manual artefact identification. We recently developed the fully automated Reduction of Electroencephalographic Artefacts (RELAX) pipeline and demonstrated its performance in cleaning EEG data recorded from adult populations. Here, we introduce the RELAX-Jr pipeline, which was adapted from RELAX and designed specifically for pre-processing of data collected from children. RELAX-Jr implements multi-channel Wiener filtering (MWF) and/or wavelet-enhanced independent component analysis (wICA) combined with the adjusted-ADJUST automated independent component classification algorithm to identify and reduce all artefacts using algorithms adapted to optimally identify artefacts in EEG recordings taken from children. Using a dataset of resting-state EEG recordings (N = 136) from children spanning early-to-middle childhood (4-12 years), we assessed the cleaning performance of RELAX-Jr using a range of metrics including signal-to-error ratio, artefact-to-residue ratio, ability to reduce blink and muscle contamination, and differences in estimates of alpha power between eyes-open and eyes-closed recordings. We also compared the performance of RELAX-Jr against four publicly available automated cleaning pipelines. We demonstrate that RELAX-Jr provides strong cleaning performance across a range of metrics, supporting its use as an effective and fully automated cleaning pipeline for neurodevelopmental EEG data.

An All‐In‐One Rapid Prediction of Ground Motion Intensity Measures Hybrid Network for Multi‐Task in the North‐South Seismic Belt of China

AbstractThe north‐south seismic belt of China poses a high risk of earthquakes, necessitating the need for accurate and rapid prediction of intensity measures (IMs) to prevent and mitigate potential damage. We have developed a new multi‐task model, CRAQuake, to predict IMs for the north‐south seismic belt of China. Using initial arrival seismic waves recorded at a single station as input, CRAQuake simultaneously predicts six IMs without relying on pre‐configured parameters such as earthquake source, path, and location. The model was trained on 4,281 sets of strong motion records data sets at 822 stations and tested to show highly correlated results with the target IMs. The prediction performance continues to improve as the input initial arrival seismic wave time window increases. CRAQuake promises to enhance the accuracy and timeliness of IMs prediction in the north‐south seismic belt of China.

Putting People First in Data Quality: Feminist Data Ethics for Open Government Datasets

ABSTRACTOpen government information systems offer great potential for advancing civic life and democracy, but they also reflect and reinforce the biases and systematic inequalities faced by members of socially marginalized groups. We present results from a critical data modeling project that uses a data quality framework to examine open datasets published by police departments in order to understand how data modeling choices shape the social impact of these datasets. Using an arrest record dataset published by the Los Angeles Police Department as a case study, we present we present results detailing the representation of racial data and the presence of children in the dataset. We argue that current data quality frameworks for open government data are insufficient for critical data studies due to an orientation around institutional and computational interests. Incorporating feminist data ethics into data quality analysis provides an approach to data quality that centers people and communities. We propose a definition for data quality of open government datasets based on an ethics of care that centers the needs of vulnerable populations and accountability of institutions toward their communities.