A distributed data processing scheme based on Hadoop for synchrotron radiation experiments

With the development of synchrotron radiation sources and high-frame-rate detectors, the amount of experimental data collected at synchrotron radiation beamlines has increased exponentially. As a result, data processing for synchrotron radiation experiments has entered the era of big data. It is becoming increasingly important for beamlines to have the capability to process large-scale data in parallel to keep up with the rapid growth of data. Currently, there is no set of data processing solutions based on the big data technology framework for beamlines. Apache Hadoop is a widely used distributed system architecture for solving the problem of massive data storage and computation. This paper presents a set of distributed data processing schemes for beamlines with experimental data using Hadoop. The Hadoop Distributed File System is utilized as the distributed file storage system, and Hadoop YARN serves as the resource scheduler for the distributed computing cluster. A distributed data processing pipeline that can carry out massively parallel computation is designed and developed using Hadoop Spark. The entire data processing platform adopts a distributed microservice architecture, which makes the system easy to expand, reduces module coupling and improves reliability.

Device-Measured Physical Activity in 3506 Individuals with Knee or Hip Arthroplasty

PURPOSE

Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity.

METHODS

This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr). Multivariable linear regression was performed to assess step count, cadence, overall acceleration, and activity behaviors between nonarthritic controls, end-stage arthritic, and postoperative cohorts, controlling for demographic and behavioral confounders. From a cohort of 94,707 participants with valid accelerometer wear time and complete self-reported data, electronic health records were used to identify 3506 participants having undergone primary or revision hip or knee arthroplasty and 68,389 nonarthritic controls.

RESULTS

End-stage hip or knee arthritis was associated with taking 1129 fewer steps per day (95% confidence interval (CI), 811-1447; P < 0.001) and having 5.8 fewer minutes per day (95% CI, 3.0-8.7; P < 0.001) of moderate-to-vigorous activity compared with nonarthritic controls. Unilateral primary hip and knee arthroplasties were associated with 877 (95% CI, 284-1471; P = 0.004) and 893 (95% CI, 232-1554; P = 0.008) more steps than end-stage osteoarthritic participants, respectively. Postoperative unilateral hip arthroplasty participants demonstrated levels of moderate-to-vigorous physical activity and daily step count equivalent to nonarthritic controls. No difference in physical activity was observed between any cohorts in terms of overall acceleration, or time spent in daily light activity, sedentary behavior, or sleep.

CONCLUSIONS

Hip and knee arthroplasties are associated with higher levels of physical activity compared with participants with end-stage arthritis. Unilateral hip arthroplasty patients, in particular, demonstrate equivalence to nonarthritic peers at more than 1 yr after surgery.

Integrating perspectives in developing mycorrhizal trait databases: a call for inclusive and collaborative continental efforts

Global assessments of mycorrhizal symbiosis present large sampling gaps in rich biodiversity regions. Filling these gaps is necessary to build large-scale, unbiased mycorrhizal databases to obtain reliable analyses and prevent misleading generalizations. Underrepresented regions in mycorrhizal research are mainly in Africa, Asia, and South America. Despite the high biodiversity and endemism in these regions, many groups of organisms remain understudied, especially mycorrhizal fungi. In this Viewpoint, we emphasize the importance of inclusive and collaborative continental efforts in integrating perspectives for comprehensive trait database development and propose a conceptual framework that can help build large mycorrhizal databases in underrepresented regions. Based on the four Vs of big data (volume, variety, veracity, and velocity), we identify the main challenges of constructing a large mycorrhizal dataset and propose solutions for each challenge. We share our collaborative methodology, which involves employing open calls and working groups to engage all mycorrhizal researchers in the region to build a South American Mycorrhizal Database. By fostering interdisciplinary collaborations and embracing a continental-scale approach, we can create robust mycorrhizal trait databases that provide valuable insights into the evolution, ecology, and functioning of mycorrhizal associations, reducing the geographical biases that are so common in large-scale ecological studies.

Information density as a predictor of communication dynamics

In a recent paper, Aceves and Evans computed information and semantic density measures for hundreds of languages, and showed that these measures predict the pace and breadth of ideas in communication. Here, we summarize their key findings and situate them in a broader debate about the adaptive nature of language.

Sex classification from functional brain connectivity: Generalization to multiple datasets

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.

Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis

Background

Digital health has been taught at medical faculties for a few years. However, in general, the teaching of digital competencies in medical education and training is still underrepresented.

Objective

This study aims to analyze the objective acquisition of digital competencies through the implementation of a transdisciplinary digital health curriculum as a compulsory elective subject at a German university. The main subject areas of digital leadership and management, digital learning and didactics, digital communication, robotics, and generative artificial intelligence were developed and taught in a transdisciplinary manner over a period of 1 semester.

Methods

The participants evaluated the relevant content of the curriculum regarding the competencies already taught in advance during the study, using a Likert scale. The participants' increase in digital competencies were examined with a pre-post test consisting of 12 questions. Statistical analysis was performed using an unpaired 2-tailed Student t test. A P value of <.05 was considered statistically significant. Furthermore, an analysis of the acceptance of the transdisciplinary approach as well as the application of an alternative examination method (term paper instead of a test with closed and open questions) was carried out.

Results

In the first year after the introduction of the compulsory elective subject, students of human medicine (n=15), dentistry (n=3), and medical biotechnology (n=2) participated in the curriculum. In total, 13 participants were women (7 men), and 61.1% (n=11) of the participants in human medicine and dentistry were in the preclinical study stage (clinical: n=7, 38.9%). All the aforementioned learning objectives were largely absent in all study sections (preclinical: mean 4.2; clinical: mean 4.4; P=.02). The pre-post test comparison revealed a significant increase of 106% in knowledge (P<.001) among the participants.

Conclusions

The transdisciplinary teaching of a digital health curriculum, including digital teaching methods, considers perspectives and skills from different disciplines. Our new curriculum facilitates an objective increase in knowledge regarding the complex challenges of the digital transformation of our health care system. Of the 16 student term papers arising from the course, robotics and artificial intelligence attracted the most interest, accounting for 9 of the submissions.

Risk of Severe COVID-19 and Protective Effectiveness of Vaccination Among Solid Organ Transplant Recipients

BACKGROUND

Solid organ transplant recipients (SOTRs) are at higher risk for severe infection. However, the risk for severe COVID-19 and vaccine effectiveness among SOTRs remain unclear.

METHODS

This retrospective study used a nationwide health care claims database and COVID-19 registry from the Republic of Korea (2020 to 2022). Adult SOTRs diagnosed with COVID-19 were matched with up to 4 non-SOTR COVID-19 patients by propensity score. Severe COVID-19 was defined as treatment with high-flow nasal cannulae, mechanical ventilation, or extracorporeal membrane oxygenation.

RESULTS

Among 6783 SOTRs with COVID-19, severe COVID-19 was reported with the highest rate in lung transplant recipients (13.16%), followed by the heart (6.30%), kidney (3.90%), and liver (2.40%). SOTRs had a higher risk of severe COVID-19 compared to non-SOTRs, and lung transplant recipients showed the highest risk (adjusted odds ratio, 18.14; 95% confidence interval [CI], 8.53-38.58). Vaccine effectiveness against severe disease among SOTRs was 47% (95% CI, 18%-65%), 64% (95% CI, 49%-75%), and 64% (95% CI, 29%-81%) for 2, 3, and 4 doses, respectively.

CONCLUSIONS

SOTRs are at significantly higher risk for severe COVID-19 compared to non-SOTRs. Vaccination is effective in preventing the progression to severe COVID-19. Efforts should be made to improve vaccine uptake among SOTRs, while additional protective measures should be developed.

Balancing Protection and Quality in Big Data Analytics Pipelines

Existing data engine implementations do not properly manage the conflict between the need of protecting and sharing data, which is hampering the spread of big data applications and limiting their impact. These two requirements have often been studied and defined independently, leading to a conceptual and technological misalignment. This article presents the architecture and technical implementation of a data engine addressing this conflict by integrating a new governance solution based on access control within a big data analytics pipeline. Our data engine enriches traditional components for data governance with an access control system that enforces access to data in a big data environment based on data transformations. Data are then used along the pipeline only after sanitization, protecting sensitive attributes before their usage, in an effort to facilitate the balance between protection and quality. The solution was tested in a real-world smart city scenario using the data of the Oslo city transportation system. Specifically, we compared the different predictive models trained with the data views obtained by applying the secure transformations required by different user roles to the same data set. The results show that the predictive models, built on data manipulated according to access control policies, are still effective.

Decreased incidence of Kawasaki disease in South Korea during the SARS-CoV-2 pandemic

Purpose

Analyzing Kawasaki disease epidemiology during the SARS-CoV-2 pandemic in South Korea using 2012-2020 National Health Insurance Service data.

Methods

The incidence of Kawasaki disease for 2012-2020 was investigated to identify changes in incidence after the start of the pandemic. National Health Insurance Service data from the Republic of Korea were used. Kawasaki disease was defined based on the International Statistical Classification of Diseases and Related Health Problems, the Tenth Revision diagnostic code (M30.3), and the intravenous immunoglobulin prescription code. Prescription history was collected for the following medications: intravenous immunoglobulin, aspirin, corticosteroids, tumor necrosis factor-α antagonist, clopidogrel, and anticoagulation drugs.

Results

The Kawasaki disease incidence per 100,000 individuals younger than 5 years was 238.9, 230.0, and 141.2 in 2018, 2019, and 2020, respectively. Regarding the incidence from 2012 to 2020, it was the highest in 2018 and decreased to 141.2 (p < 0.001) in 2020, after the start of the pandemic. In 2020, 28.3% of all patients with KD were infants, a percentage significantly higher than that of the previous year (p < 0.001). There was biphasic seasonality in the monthly Kawasaki disease incidence. The Kawasaki disease incidence was the highest in winter followed by that in early summer.

Conclusion

After the start of the pandemic, the Kawasaki disease incidence decreased, and the percentage of patients with Kawasaki disease aged <1 year increased. These findings provide support for the hypothesis suggesting an infectious trigger in Kawasaki disease.

Redefining governance: a critical analysis of sustainability transformation in e-governance

With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.

Measuring the Impact of Primary Care Team Composition on Patient Activation Utilizing Electronic Health Record Big Data Analytics

Purpose

Team-based care has been linked to key outcomes associated with the Quadruple Aim and a key driver of high-value patient-centered care. Use of the electronic health record (EHR) and machine learning have significant potential to overcome previous barriers to studying the impact of teams, including delays in accessing data to improve teamwork and optimize patient outcomes.

Methods

This study utilized a large EHR dataset (n=316,542) from an urban health system to explore the relationship between team composition and patient activation, a key driver of patient engagement. Teams were operationalized using consensus definitions of teamwork from the literature. Patient activation was measured using the Patient Activation Measure (PAM). Results from multilevel regression analyses were compared to machine learning analyses using multinomial logistic regression to calculate propensity scores for the effect of team composition on PAM scores. Under the machine learning approach, a causal inference model with generalized overlap weighting was used to calculate the average treatment effect of teamwork.

Results

Seventeen different team types were observed in the data from the analyzed sample (n=12,448). Team sizes ranged from 2 to 5 members. After controlling for confounding variables in both analyses, more diverse, multidisciplinary teams (team size of 4 or more) were observed to have improved patient activation scores.

Conclusions

This is the first study to explore the relationship between team composition and patient activation using the EHR and big data analytics. Implications for further research using EHR data and machine learning to study teams and other patient-centered care are promising and could be used to advance team science.

Global Research Status Regarding Uveitis in the Last Decade

PURPOSE

To provide an overview on global uveitis research in the last decade in terms of countries/regions, organizations, scholars, journals, trending topics, and fundings.

METHODS

This cross-sectional bibliometric analysis yielded 10656 uveitis publications in English for subsequent bibliometric analysis.

RESULTS

In terms of the number of publications, the leading country/region was the USA (3007). The most productive organization was the University College London (420). The most productive research team was Professor Yang's group (146). A higher h-index was noted in University College London (48). Professor Rosenbaum was the first h-index holder (32). Keywords of interest included topics such as biologics, COVID and OCT. Publications by Ocular Immunology and Inflammation (968) ranked the first position.

CONCLUSIONS

The USA is the leading force in uveitis study. Asian countries/regions, such as China (mainland) and India, are exerting a substantial role worldwide. Trendy topics cover COVID-19, OCTA.

Heterogeneous Association of Tooth Loss with Functional Limitations

Tooth loss is prevalent in older adults and associated with functional capacity decline. Studies on the susceptibility of some individuals to the effects of tooth loss are lacking. This study aimed to investigate the heterogeneity of the association between tooth loss and higher-level functional capacity in older Japanese individuals employing a machine learning approach. This is a prospective cohort study using the data of adults aged ≥65 y in Japan (N = 16,553). Higher-level functional capacity, comprising instrumental independence, intellectual activity, and social role, was evaluated using the Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG-IC). The scale ranged from 0 (lowest function) to 13 (highest function). Doubly robust targeted maximum likelihood estimation was used to estimate the population-average association between tooth loss (having <20 natural teeth) and TMIG-IC total score after 6 y. The heterogeneity of the association was evaluated by estimating conditional average treatment effects (CATEs) using the causal forest algorithm. The result showed that tooth loss was statistically significantly associated with lower TMIG-IC total scores (population-average effect: -0.14; 95% confidence interval, -0.18 to -0.09). The causal forest analysis revealed the heterogeneous associations between tooth loss and lower TMIG-IC total score after 6 y (median of estimated CATEs = -0.13; interquartile range = 0.12). The high-impact subgroup (i.e., individuals with estimated CATEs of the bottom 10%) were significantly more likely to be older and male, had a lower socioeconomic status, did not have a partner, and had poor health conditions compared with the low-impact subgroup (i.e., individuals with estimated CATEs of the top 10%). This study found that heterogeneity exists in the association between tooth loss and lower scores on functional capacity. Implementing tooth loss prevention policy and clinical measures, especially among vulnerable subpopulations significantly affected by tooth loss, may reduce its burden more effectively.

Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

Doctor's Orders-Why Radiologists Should Consider Adjusting Commercial Machine Learning Applications in Chest Radiography to Fit Their Specific Needs

This retrospective study evaluated a commercial deep learning (DL) software for chest radiographs and explored its performance in different scenarios. A total of 477 patients (284 male, 193 female, mean age 61.4 (44.7-78.1) years) were included. For the reference standard, two radiologists performed independent readings on seven diseases, thus reporting 226 findings in 167 patients. An autonomous DL reading was performed separately and evaluated against the gold standard regarding accuracy, sensitivity and specificity using ROC analysis. The overall average AUC was 0.84 (95%-CI 0.76-0.92) with an optimized DL sensitivity of 85% and specificity of 75.4%. The best results were seen in pleural effusion with an AUC of 0.92 (0.885-0.955) and sensitivity and specificity of each 86.4%. The data also showed a significant influence of sex, age, and comorbidity on the level of agreement between gold standard and DL reading. About 40% of cases could be ruled out correctly when screening for only one specific disease with a sensitivity above 95% in the exploratory analysis. For the combined reading of all abnormalities at once, only marginal workload reduction could be achieved due to insufficient specificity. DL applications like this one bear the prospect of autonomous comprehensive reporting on chest radiographs but for now require human supervision. Radiologists need to consider possible bias in certain patient groups, e.g., elderly and women. By adjusting their threshold values, commercial DL applications could already be deployed for a variety of tasks, e.g., ruling out certain conditions in screening scenarios and offering high potential for workload reduction.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Similar risk of kidney failure among patients with blinding diseases who receive ranibizumab, aflibercept, and bevacizumab: an OHDSI Network Study

OBJECTIVE OR PURPOSE

A) To characterize the incidence of kidney failure associated with intravitreal anti-vascular endothelial growth factor (VEGF) exposure, and B) compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab.

DESIGN

Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network.

SUBJECTS, PARTICIPANTS, AND/OR CONTROLS

Subjects aged ≥18 years with ≥3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion).

METHODS, INTERVENTION, OR TESTING

A) The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. B) For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random-effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate.

MAIN OUTCOME MEASURES

Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure.

RESULTS

Of the 6.1 million patients with blinding diseases, 37,189 who received ranibizumab, 39,447 aflibercept, and 163,611 bevacizumab were included; the total treatment exposure time was 161,724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100,000 persons (range 0 to 2389), and incidence rate 743 per 100,000 person-years (0 to 2661). The meta-analysis HR of kidney failure comparing aflibercept to ranibizumab was 1.01 (95% confidence interval (CI) 0.70, 1.47, p=0.45), ranibizumab to bevacizumab 0.95 (95% CI 0.68, 1.32, p=0.62), and aflibercept to bevacizumab 0.95 (95% CI 0.65, 1.39, p=0.60).

CONCLUSIONS

There was no substantially different relative risk for kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk for kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples

BACKGROUND

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

METHODS

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

RESULTS

In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

CONCLUSION

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach

Introduction

Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors.

Methods

We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs.

Results

Logistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43).

Discussion

Our machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

BACKGROUND

Gabaldi et al. utilized telemedicine data, web search trends, hospitalized patient characteristics, and resource usage data to estimate bed occupancy during the COVID-19 pandemic. The results showcase the potential of data-driven strategies to enhance resource allocation decisions for an effective pandemic response.

OBJECTIVE

To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil.

METHODS

Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022.

RESULTS

The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days.

CONCLUSION

The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

BACKGROUND

Developed models to forecast bed occupancy for up to 14 days and monitored errors for 365 days.

BACKGROUND

Telemedicine calls from COVID-19 patients correlated with the number of patients hospitalized in the next 8 days.

Digital twins in sustainable transition: exploring the role of EU data governance

Introduction

Digital twins can accelerate sustainable development by leveraging big data and artificial intelligence to simulate state, reactions and potential developments of physical systems. In doing so, they can create a comprehensive basis for data-driven policy decisions. One of the purposes of digital twins is to facilitate the implementation of the EU's Green Deal-in line with internationally binding climate and environmental targets. One prerequisite for the success of digital twins is a comprehensive, high-quality database. This requires a suitable legal framework that ensures access to such data.

Methods

Applying a qualitative governance analysis, the following article examines if the EU's strategies and legal acts on data governance are paving the way for digital twin projects which promote sustainability.

Results

Results show important starting points for open and fair data use within the growing field of EU digital law. However, there is still a lot of progress to be made to legally link the use of digital twins with binding sustainability objectives.

Pubertal gynecomastia incidence among 530,000 boys: a cross sectional population based study

Background

Adolescent gynecomastia, a benign proliferation of male breast tissue, can lead to psychological issues during adolescence. The prevalence varies widely (4%-69%). The incidence peaks are during neonatal, pubertal, and senescent periods. Its affect on emotional well-being necessitates understanding and occasional intervention. This study aimed to determine the incidence of gynecomastia among male adolescents aged 12-15 years.

Methods

A retrospective cross-sectional study utilized the Clalit Health Care Services database (2008-2021) with a population of approximately 4.5 million. Participants aged 12-15 years were included if diagnosed with gynecomastia (International classification of diseases-9 code 611.1) and having a body mass index (BMI) measurement and no obesity diagnosis (ICD9 code 278.0). Data analysis included incidence rates and associations with ethnicity, age, BMI, and socioeconomic status.

Results

531,686 participants included with an incidence of 1.08%. Of all participants, 478,140 had a BMI ≤ 25 with an incidence of 0.7%, and 0.25%-0.35% yearly, and 70% of gynecomastia patients were aged 13-14 years. The prevalence of gynecomastia differed between Jews (1.28%) and Arabs (0.67%), but the disparity diminished when socioeconomic status was considered.

Conclusions

This unprecedented Population study establishes a definitive rate of true pubertal gynecomastia, revealing a lower yearly incidence as compared to previous reports. The higher observed prevalence among Jewish adolescents, may be caused due to complex interactions between different influencing factors. Understanding these dynamics can aid in formulating more targeted interventions and policy strategies to address gynecomastia's affect on adolescent well-being.

Durability of the Effectiveness of Heterologous COVID-19 Vaccine Regimens in Thailand: Retrospective Cohort Study Using National Registration Data

BACKGROUND

The durability of heterologous COVID-19 vaccine effectiveness (VE) has been primarily studied in high-income countries, while evaluation of heterologous vaccine policies in low- and middle-income countries remains limited.

OBJECTIVE

We aimed to evaluate the duration during which the VE of heterologous COVID-19 vaccine regimens in mitigating serious outcomes, specifically severe COVID-19 and death following hospitalization with COVID-19, remains over 50%.

METHODS

We formed a dynamic cohort by linking records of Thai citizens aged ≥18 years from citizen vital, COVID-19 vaccine, and COVID-19 cases registry databases between May 2021 and July 2022. Encrypted citizen identification numbers were used to merge the data between the databases. This study focuses on 8 common heterologous vaccine sequences: CoronaVac/ChAdOx1, ChAdOx1/BNT162b2, CoronaVac/CoronaVac/ChAdOx1, CoronaVac/ChAdOx1/ChAdOx1, CoronaVac/ChAdOx1/BNT162b2, BBIBP-CorV/BBIBP-CorV/BNT162b2, ChAdOx1/ChAdOx1/BNT162b2, and ChAdOx1/ChAdOx1/mRNA-1273. Nonimmunized individuals were considered for comparisons. The cohort was stratified according to the vaccination status, age, sex, province location, month of vaccination, and outcome. Data analysis employed logistic regression to determine the VE, accounting for potential confounders and durability over time, with data observed over a follow-up period of 7 months.

RESULTS

This study includes 52,580,841 individuals, with approximately 17,907,215 and 17,190,975 receiving 2- and 3-dose common heterologous vaccines (not mutually exclusive), respectively. The 2-dose heterologous vaccinations offered approximately 50% VE against severe COVID-19 and death following hospitalization with COVID-19 for 2 months; however, the protection significantly declined over time. The 3-dose heterologous vaccinations sustained over 50% VE against both outcomes for at least 8 months, as determined by logistic regression with durability time-interaction modeling. The vaccine sequence consisting of CoronaVac/CoronaVac/ChAdOx1 demonstrated >80% VE against both outcomes, with no evidence of VE waning. The final monthly measured VE of CoronaVac/CoronaVac/ChAdOx1 against severe COVID-19 and death following hospitalization at 7 months after the last dose was 82% (95% CI 80.3%-84%) and 86.3% (95% CI 83.6%-84%), respectively.

CONCLUSIONS

In Thailand, within a 7-month observation period, the 2-dose regimens could not maintain a 50% VE against severe and fatal COVID-19 for over 2 months, but all of the 3-dose regimens did. The CoronaVac/CoronaVac/ChAdOx1 regimen showed the best protective effect against severe and fatal COVID-19. The estimated durability of 50% VE for at least 8 months across all 3-dose heterologous COVID-19 vaccine regimens supports the adoption of heterologous prime-boost vaccination strategies, with a primary series of inactivated virus vaccine and boosting with either a viral vector or an mRNA vaccine, to prevent similar pandemics in low- and middle-income countries.

Clinical profile, demographic distribution, and management of Posner-Schlossman syndrome: An electronic medical record-driven data analytics from an eye care network in India

PURPOSE

To describe the clinical profile, demographics, and management of Posner-Schlossman syndrome (PSS) in patients presenting to a multitier ophthalmology hospital network in India.

METHODS

This cross-sectional hospital-based study included 3,082,727 new patients presenting between August 2010 and December 2021. Patients with a clinical diagnosis of PSS in at least one eye were included as cases. The data were collected using an electronic medical record system.

RESULTS

Overall, 130 eyes of 126 (0.004%) patients were diagnosed with PSS. The majority of the patients were male (81.75%) and had unilateral (96.83%) affliction. The most common age group at presentation was during the fourth decade of life, with 46 (36.5%) patients. The overall prevalence was higher in patients from a higher socioeconomic status (0.005%) presenting from the metropolitan geography (0.008%) and in professionals (0.014%). A significant number of patients (108; 83.08%) had a raised intraocular pressure of >30 mm of Hg. The majority of the eyes had mild or no visual impairment (better than 20/70) in 99 (76.15%) eyes. Keratic precipitates were found in 59 (45.38%) eyes, anterior chamber cells in 43 (33.08%) eyes, and iris atrophy in seven (5.38%) eyes. The majority of eyes (127; 97.69%) had open angles on gonioscopy. The average duration of use of topical steroids was 1.70 ± 0.76 months, and the average duration of use of topical antiglaucoma medications (AGMs) was 1.66 ± 0.81 months, with 35 eyes (26.92%) requiring continued AGMs. Among the surgical interventions, trabeculectomy was performed in nine (6.92%) eyes and cataract surgery in five (3.85%) eyes.

CONCLUSION

PSS more commonly affects males presenting during the fourth decade of life from higher socioeconomic status and is predominantly unilateral. The majority of the eyes have mild or no visual impairment, open angles, and require surgical intervention in a tenth of the eyes.

Optimizing large real-world data analysis with parquet files in R: A step-by-step tutorial

PURPOSE

The use of open-source programming languages can facilitate open science practices in real-world evidence (RWE) studies. Real-world studies often rely on using big data, which makes using such languages complicated. We demonstrate an efficient approach that enables RWE researchers to use R to undertake RWE analysis tasks from cohort building to reporting.

METHODS

Using the Merative Marketscan data (2017-2019), we developed an R function to transform the data into parquet format to be used in R. Then, we compared the differences in data size before and after transformation. We compared the performance of the transformed data in R to the original data in terms of numerical consistency and running times required to complete simple exploratory tasks. To show how the transformed databases can be used in practice, we conducted a simplified replication of an active comparator new user study from the literature. All codes are available on GitHub.

RESULTS

Our approach exhibited high efficiency in data storage, as evidenced by the converted data size, which ranged from 10% to 43% of that of the original data files. The runtime of the exploratory tasks in R generally outperformed that of the original data with SAS. We showed, through example, how the converted data can be efficiently used to implement an RWE study.

CONCLUSION

We demonstrate a free and efficient solution to facilitate the use of open-source programming languages with big real-world databases, which can facilitate the adoption of open science practices.

Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.

Personalized laboratory medicine in the digital health era: recent developments and future challenges

Interpretation of laboratory data is a comparative procedure and requires reliable reference data, which are mostly derived from population data but used for individuals in conventional laboratory medicine. Using population data as a "reference" for individuals has generated several problems related to diagnosing, monitoring, and treating single individuals. This issue can be resolved by using data from individuals' repeated samples, as their personal reference, thus needing that laboratory data be personalized. The modern laboratory information system (LIS) can store the results of repeated measurements from millions of individuals. These data can then be analyzed to generate a variety of personalized reference data sets for numerous comparisons. In this manuscript, we redefine the term "personalized laboratory medicine" as the practices based on individual-specific samples and data. These reflect their unique biological characteristics, encompassing omics data, clinical chemistry, endocrinology, hematology, coagulation, and within-person biological variation of all laboratory data. It also includes information about individuals' health behavior, chronotypes, and all statistical algorithms used to make precise decisions. This approach facilitates more accurate diagnosis, monitoring, and treatment of diseases for each individual. Furthermore, we explore recent advancements and future challenges of personalized laboratory medicine in the context of the digital health era.

Leveraging big data for causal understanding in mental health: a research framework

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

Farming activity and risk of treated thyroid disorders: Insights from the TRACTOR project, a nationwide cohort study

BACKGROUND

Epidemiological data regarding thyroid diseases are lacking, in particular for occupationally exposed populations.

OBJECTIVES

To compare the risk of hypothyroidism and hyperthyroidism between farming activities within the complete population of French farm managers (FMs).

METHODS

Digital health data from retrospective administrative databases, including insurance claims and electronic health/medical records, was employed. This cohort data spanned the entirety of French farm managers (FMs) who had undertaken work at least once from 2002 to 2016. Survival analysis with the time to initial medication reimbursement as timescale was used to examine the association (hazard ratio, HR) between 26 specific farming activities and both treated hypothyroidism and hyperthyroidism. A distinct model was developed for each farming activity, comparing FMs who had never engaged in the specific farming activity between 2002 and 2016 with those who had. All analyses were adjusted for potential confounders (e.g., age), and sensitivity analyses were conducted.

RESULTS

Among 1088561 FMs (mean age 46.6 [SD 14.1]; 31% females), there were 31834 hypothyroidism cases (75% females) and 620 hyperthyroidism cases (67% females), respectively. The highest risks were observed for cattle activities for both hyperthyroidism (HR ranging from 1.75 to 2.42) and hypothyroidism (HR ranging from 1.41 to 1.44). For hypothyroidism, higher risks were also observed for several animal farming activities (pig, poultry, and rabbit), as well as fruit arboriculture (HR = 1.22 [1.14-1.31]). The lowest risks were observed for activities involving horses. Sex differences in the risk of hypothyroidism were observed for eight activities, with the risk being higher for males (HR = 1.09 [1.01-1.20]) than females in viticulture (HR = 0.97 [0.93-1.00]). The risk of hyperthyroidism was two times higher for male dairy farmers than females.

DISCUSSION

Our findings offer a comprehensive overview of thyroid disease risks within the FM community. Thyroid ailments might not stem from a single cause but likely arise from the combined effects of various causal agents and triggering factors (agricultural exposome). Further investigation into distinct farming activities-especially those involving cattle-is essential to pinpoint potential risk factors that could enhance thyroid disease monitoring in agriculture.

Explainable Model Using Shapley Additive Explanations Approach on Wound Infection after Wide Soft Tissue Sarcoma Resection: "Big Data" Analysis Based on Health Insurance Review and Assessment Service Hub

Background and Objectives: Soft tissue sarcomas represent a heterogeneous group of malignant mesenchymal tissues. Despite their low prevalence, soft tissue sarcomas present clinical challenges for orthopedic surgeons owing to their aggressive nature, and perioperative wound infections. However, the low prevalence of soft tissue sarcomas has hindered the availability of large-scale studies. This study aimed to analyze wound infections after wide resection in patients with soft tissue sarcomas by employing big data analytics from the Hub of the Health Insurance Review and Assessment Service (HIRA). Materials and Methods: Patients who underwent wide excision of soft tissue sarcomas between 2010 and 2021 were included. Data were collected from the HIRA database of approximately 50 million individuals' information in the Republic of Korea. The data collected included demographic information, diagnoses, prescribed medications, and surgical procedures. Random forest has been used to analyze the major associated determinants. A total of 10,906 observations with complete data were divided into training and validation sets in an 80:20 ratio (8773 vs. 2193 cases). Random forest permutation importance was employed to identify the major predictors of infection and Shapley Additive Explanations (SHAP) values were derived to analyze the directions of associations with predictors. Results: A total of 10,969 patients who underwent wide excision of soft tissue sarcomas were included. Among the study population, 886 (8.08%) patients had post-operative infections requiring surgery. The overall transfusion rate for wide excision was 20.67% (2267 patients). Risk factors among the comorbidities of each patient with wound infection were analyzed and dependence plots of individual features were visualized. The transfusion dependence plot reveals a distinctive pattern, with SHAP values displaying a negative trend for individuals without blood transfusions and a positive trend for those who received blood transfusions, emphasizing the substantial impact of blood transfusions on the likelihood of wound infection. Conclusions: Using the machine learning random forest model and the SHAP values, the perioperative transfusion, male sex, old age, and low SES were important features of wound infection in soft-tissue sarcoma patients.

Consore: A Powerful Federated Data Mining Tool Driving a French Research Network to Accelerate Cancer Research

BACKGROUND

Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects.

METHODS

UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network: Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals.

RESULTS

Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. These data originate from their electronic medical records, encompassing about 65 million medical records. Thanks to the structured data, which are harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers.

CONCLUSIONS

Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance with regulations and acceleration of cancer research projects.

Data sharing and big data in health professions education: Ottawa consensus statement and recommendations for scholarship

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.

Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.

Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning

The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highlights the opportunities to diagnose lifetime abnormalities via "big data" analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost-benefit, and improved environmental friendliness.

Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm

We analyzed the symptoms composition of Interstitial Cystitis (IC), the regularity of the evolution of symptoms before and after treatment, and the visualization of the community network, to provide a reference for clinical diagnosis and treatment of Interstitial Cystitis. Based on the outpatient electronic case data of 552 patients with Interstitial Cystitis, we used a complex network community discovery algorithm, directed weighted complex network, and Sankey map to mine the data of the symptoms composition of Interstitial Cystitis, the evolution of symptoms before and after treatment and the visualization of the community network, to analyze the epidemiological characteristics of interstitial cystitis symptoms in the real world. By the community division of the complex network of interstitial cystitis symptoms, We finally obtained three core symptom communities. Among them, symptom community A (bladder-related symptoms) is the symptom community with the highest proportion of nodes (60.00%) in the complex network of Interstitial Cystitis, symptom community B (non-bladder-related symptoms 1) ranks second (32.00%) in a complex network of Interstitial Cystitis, and symptom community C (non-bladder-related symptoms 2) has the lowest proportion (8.00%). There is a complex evolutionary relationship between the symptoms of Interstitial Cystitis before and after treatment. Among the single symptoms before and after treatment, the decreased rate of Day frequency is 93.22%, and the reduced urgency rate is 93.07%. The decline rate of Nocturia was 82.33%. From the perspective of different communities, the overall symptoms of symptom community A decreased by 34.39% after treatment, the general symptoms of symptom community B decreased by 35.37%, and the prevalent symptoms of symptom community C decreased by 71.43%. In the case of using diet regulation treatment to treat bladder pain, the cure rate of bladder pain can reach 22.67%. The cure rate of burning in bladders can get 15.38% with Percutaneous Sacral neuromodulation, 96.95% with diet regulation treatment, and 100% with Percutaneous Sacral neuromodulation. When using behavioral physiotherapy to treat bladder pain, 3.57% of the patient's symptoms change to bladder discomfort; 4% of the patient's symptoms change to bladder discomfort when using oral medicine to treat bladder pain.Symptom research methods based on community findings can effectively explore the rule of symptom outcome of Interstitial Cystitis before and after treatment, and the results are highly interpretable by professionals. The cover image is based on the Original Article Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm by Lei Pang et al., https://doi.org/10.1049/syb2.12083.

Big data and artificial intelligence in cancer research

The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.

Academic data science: Transdisciplinary and extradisciplinary visions

As a nascent field within the academy, the contours, attributes, and bounties of data science are still indeterminate and contested. We studied how participants in an initiative to establish data science at a large American research university defined data science and articulated their relationships to the field. We discuss two contrasting visions for data science among our research participants. One vision is a transdisciplinary view portraying data science as a phenomenon with transcendent, appropriative, and impositional qualities that sits apart from academic domains. Another view of data science-one that was far more prevalent among our research subjects-casts data science as grounded, relational, and adaptive, emerging from crosspollination of numerous academic domains. We argue that this latter formulation represents a more quotidian reality of data science and positions the field as an extradiscipline, defined as a field that exists to facilitate the exchange of knowledge, skills, tools, and methods from an indeterminate and fluctuating set of disciplinary perspectives while conserving the boundaries of those disciplines. We argue that the dueling transdisciplinary and extradisciplinary visions for data science have important implications for how the field will mature, and that the extradiscipline concept opens novel directions for studying academic knowledge production in STS, contributing additional precision to the literature on disciplinarity and its permutations.

Beyond 'single customer view': Player tracking's potential role in understanding and reducing gambling-related harm

BACKGROUND

Usage of electronic gaming machines (EGMs) and on-line gambling is strongly associated with gambling-related harm. Player-tracking systems can monitor a gambler's activity across multiple sessions and/or operators, providing a clearer picture of the person's risk of harm with respect to these gambling formats and enabling harm reduction efforts. The Finnish and Norwegian state monopolies have player-tracking systems in place, while the United Kingdom is implementing an operator-led system called 'single customer view' for on-line gambling, and Australian states are proposing similar 'player cards' for land-based EGMs.

ARGUMENT

Player tracking can advance harm reduction efforts in three ways. First, player tracking improves our understanding of gambling-related harm by providing data on how the population gambles, which can potentially be linked with operator, government and/or prevalence data sets. Secondly, player tracking can be used to implement harm reduction measures such as expenditure limits, self-exclusion and age verification. Thirdly, player tracking can provide a platform to evaluate harm reduction measures via gold-standard field trials. These potential benefits need to be weighed against various practical and ethical issues.

CONCLUSIONS

The potential benefits of player-tracking systems would be maximized via systems administered independently of the gambling industry and implemented universally across all gambling in a given jurisdiction.

Kidney outcomes associated with haematuria and proteinuria trajectories among patients with IgA nephropathy in real-world clinical practice: The Japan Chronic Kidney Disease Database

AIM

Among patients with Immunoglobulin A (IgA) nephropathy, we aimed to identify trajectory patterns stratified by the magnitude of haematuria and proteinuria using repeated urine dipstick tests, and assess whether the trajectories were associated with kidney events.

METHODS

Using a nationwide multicentre chronic kidney disease (CKD) registry, we analysed data from 889 patients with IgA nephropathy (mean age 49.3 years). The primary outcome was a sustained reduction in eGFR of 50% or more from the index date and thereafter. During follow-up (median 49.0 months), we identified four trajectories (low-stable, moderate-decreasing, moderate-stable, and high-stable) in both urine dipstick haematuria and proteinuria measurements, respectively.

RESULTS

In haematuria trajectory analyses, compared to the low-stable group, the adjusted hazard ratios (HRs) (95% confidence interval [CI]) for kidney events were 2.59 (95% CI, 1.48-4.51) for the high-stable, 2.31 (95% CI, 1.19-4.50) for the moderate-stable, and 1.43 (95% CI, (0.72-2.82) for the moderate-decreasing groups, respectively. When each proteinuria trajectory group was subcategorized according to haematuria trajectories, the proteinuria group with high-stable and with modest-stable haematuria trajectories had approximately 2-times higher risk for eGFR reduction ≥50% compared to that with low-stable haematuria trajectory.

CONCLUSION

Assessments of both haematuria and proteinuria trajectories using urine dipstick could identify high-risk IgA nephropathy patients.

Unlocking the potential of big data and AI in medicine: insights from biobanking

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Identifying Determinants of Survival Disparities in Multiple Myeloma Patients Using Electronic Health Record Data

Multiple myeloma (MM) is one of the most common hematological malignancies. The goal of this study was to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of multiple myeloma patients using EHR data from an academic medical center in New York City. The de-identified analytical dataset comprised 2,111 patients with MM who were stratified based on the length of survival into two groups. Demographic variables, cancer stage, income level, and genetic mutations were analyzed using descriptive statistics and logistic regression. Age, race, and cancer stage were all significant factors that affected the length of survival of multiple myeloma patients. In contrast, gender and income level were not significant factors based on the multivariate adjusted analysis. Older adults, African American patients, and patients who were diagnosed with stage III of multiple myeloma were the people most likely to exhibit short-term survival after the MM diagnosis.

Large-Scale Standardized Image Integration for Secondary Use Research Projects

Imaging techniques are a cornerstone of today's medicine and can be crucial for a successful therapy. But in addition, the generated imaging series are an important resource for new informatics' methods, especially in the field of artificial intelligence. This paper describes the success of integrating clinical routine imaging data into a standardized format for research purposes. Thus, we designed an integration flow and successfully implemented it in the local data integration center of University Hospital Schleswig-Holstein. The flow integrates imaging series and radiological reports from the primary system into an openEHR repository with enrichment by semantic codes for better findability and retrieval using HL7 FHIR. As a result, 6.6 million radiological studies with 29 million image series are now available for further medical (informatics) research.

Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words

The development of natural language processing techniques for deriving useful information from unstructured clinical narratives is a fast-paced and rapidly evolving area of machine learning research. Large volumes of veterinary clinical narratives now exist curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, and the application of such techniques to these datasets is already (and will continue to) improve our understanding of disease and disease patterns within veterinary medicine. In part one of this two part article series, we discuss the importance of understanding the lexical structure of clinical records and discuss the use of basic tools for filtering records based on key words and more complex rule based pattern matching approaches. We discuss the strengths and weaknesses of these approaches highlighting the on-going potential value in using these "traditional" approaches but ultimately recognizing that these approaches constrain how effectively information retrieval can be automated. This sets the scene for the introduction of machine-learning methodologies and the plethora of opportunities for automation of information extraction these present which is discussed in part two of the series.