Predictive value of the dynamics of absolute lymphocyte counts for 90-day mortality in ICU sepsis patients: a retrospective big data study.

OBJECTIVES: The objective of the study was to assess the clinical predictive value of the dynamics of absolute lymphocyte count (ALC) for 90-day all-cause mortality in sepsis patients in intensive care unit (ICU). DESIGN: Retrospective cohort study using big data. SETTING: This study was conducted using the Medical Information Mart for Intensive Care IV database V.2.0 database. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was 90-day all-cause mortality. PARTICIPANTS: Patients were included if they were diagnosed with sepsis on the first day of ICU admission. Exclusion criteria were ICU stay under 24 hours; the absence of lymphocyte count on the first day; extremely high lymphocyte count (>10×109/L); history of haematolymphatic tumours, bone marrow or solid organ transplants; survival time under 72 hours and previous ICU admissions. The analysis ultimately included 17 329 sepsis patients. RESULTS: The ALC in the non-survivors group was lower on days 1, 3, 5 and 7 after admission (p<0.001). The ALC on day 7 had the highest area under the curve (AUC) value for predicting 90-day mortality. The cut-off value of ALC on day 7 was 1.0×109/L. In the restricted cubic spline plot, after multivariate adjustments, patients with higher lymphocyte counts had a better prognosis. After correction, in the subgroups with Sequential Organ Failure Assessment score ≥6 or age ≥60 years, ALC on day 7 had the lowest HR value (0.79 and 0.81, respectively). On the training and testing set, adding the ALC on day 7 improved all prediction models' AUC and average precision values. CONCLUSIONS: Dynamic changes of ALC are closely associated with 90-day all-cause mortality in sepsis patients. Furthermore, the ALC on day 7 after admission is a better independent predictor of 90-day mortality in sepsis patients, especially in severely ill or young sepsis patients.

Combating Chronic Disease with Barbershop Health Interventions: A Review of Current Knowledge and Potential for Big Data.

Community-based participatory research (CBPR) using barbershop interventions is an emerging approach to address health disparities and promote health equity. Barbershops serve as trusted community settings for health education, screening services, and referrals. This narrative mini-review provides an overview of the current state of knowledge regarding CBPR employing barbershop interventions and explores the potential for big data involvement to enhance the impact and reach of this approach in combating chronic disease. CBPR using barbershop interventions has shown promising results in reducing blood pressure among Black men and improving diabetes awareness and self-management. By increasing testing rates and promoting preventive behaviors, barbershop interventions have been successful in addressing infectious diseases, including HIV and COVID-19. Barbershops have also played roles in promoting cancer screening and increasing awareness of cancer risks, namely prostate cancer and colorectal cancer. Further, leveraging the trusted relationships between barbers and their clients, mental health promotion and prevention efforts have been successful in barbershops. The potential for big data involvement in barbershop interventions for chronic disease management offers new opportunities for targeted programs, real-time monitoring, and personalized approaches. However, ethical considerations regarding privacy, confidentiality, and data ownership need to be carefully addressed. To maximize the impact of barbershop interventions, challenges such as training and resource provision for barbers, cultural appropriateness of interventions, sustainability, and scalability must be addressed. Further research is needed to evaluate long-term impact, cost-effectiveness, and best practices for implementation. Overall, barbershops have the potential to serve as key partners in addressing chronic health disparities and promoting health equity.

Nationwide Big Data Analysis of Statin Use and Intracerebral Hemorrhage Risk in Acute Ischemic Stroke Patients in Taiwan.

Background and Objectives: Although statins are recommended for secondary prevention of acute ischemic stroke, some population-based studies and clinical evidence suggest that they might be used with an increased risk of intracranial hemorrhage. In this nested case-control study, we used Taiwan's nationwide universal health insurance database to investigate the possible association between statin therapy prescribed to acute ischemic stroke patients and their risk of subsequent intracerebral hemorrhage and all-cause mortality in Taiwan. Materials and Methods: All data were retrospectively obtained from Taiwan's National Health Insurance Research Database. Acute ischemic stroke patients were divided into a cohort receiving statin pharmacotherapy and a control cohort not receiving statin pharmacotherapy. A 1:1 matching for age, gender, and index day, and propensity score matching was conducted, producing 39,366 cases and 39,366 controls. The primary outcomes were long-term subsequent intracerebral hemorrhage and all-cause mortality. The competing risk between subsequent intracerebral hemorrhage and all-cause mortality was estimated using the Fine and Gray regression hazards model. Results: Patients receiving statin pharmacotherapy after an acute ischemic stroke had a significantly lower risk of subsequent intracerebral hemorrhage (p < 0.0001) and lower all-cause mortality rates (p < 0.0001). Low, moderate, and high dosages of statin were associated with significantly decreased risks for subsequent intracerebral hemorrhage (adjusted sHRs 0.82, 0.74, 0.53) and all-cause mortality (adjusted sHRs 0.75, 0.74, 0.74), respectively. Conclusions: Statin pharmacotherapy was found to safely and effectively reduce the risk of subsequent intracerebral hemorrhage and all-cause mortality in acute ischemic stroke patients in Taiwan.

Machine learning in infectious diseases: potential applications and limitations.

Infectious diseases are a major threat for human and animal health worldwide. Artificial Intelligence (AI) combined algorithms including Machine Learning and Big Data analytics have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseases. In this commentary we explore the potential applications and limitations of ML to management of infectious disease. It explores challenges in key areas such as outbreak prediction, pathogen identification, drug discovery, and personalized medicine. We propose potential solutions to mitigate these hurdles and applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases. In addition to use of ML for management of infectious diseases, potential applications are based on catastrophic evolution events for the identification of biomolecular targets to reduce risks for infectious diseases and vaccinomics for discovery and characterization of vaccine protective antigens using intelligent Big Data analytics techniques. These considerations set a foundation for developing effective strategies for managing infectious diseases in the future.

Infectious diseases are a major challenge worldwideArtificial Intelligence (AI) combined algorithms have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseasesFuture directions include applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases.

Development of big data assisted effective enterprise resource planning framework for smart human resource management.

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Extracting big data from the internet to support the development of a new patient-reported outcome measure for breast implant illness: a proof of concept study.

PURPOSE: Individuals with health conditions often use online patient forums to share their experiences. These patient data are freely available and have rarely been used in patient-reported outcomes (PRO) research. Web scraping, the automated identification and coding of webpage data, can be employed to collect patient experiences for PRO research. The objective of this study was to assess the feasibility of using web scraping to support the development of a new PRO measure for breast implant illness (BII). METHODS: Nine publicly available BII-specific web forums were chosen post-consultation with two prominent BII advocacy leaders. The Python Selenium and Pandas packages were used to automate extraction of de-identified text from the individual posts/comments into a spreadsheet. Data were coded using a line-by-line approach and constant comparison was used to create top-level domains and sub-domains. RESULTS: 6362 unique codes were identified and organized into four top-level domains of information needs, symptom experiences, life impact of BII, and care experiences. Information needs of women included seeking/sharing information pre-breast implant surgery, post-breast implant surgery, while contemplating explant surgery, and post-explant surgery. Symptoms commonly described by women included fatigue, brain fog, and musculoskeletal symptoms. Many comments described BII's impact on daily activities and psychosocial wellbeing. Lastly, some comments described negative care experiences and experiences related to advocating for themselves to providers. CONCLUSION: This proof-of-concept study demonstrated the feasibility of employing web scraping as a cost-effective, efficient method to understand the experiences of women with BII. These data will be used to inform the development of a BII-specific PROM.

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

BACKGROUND: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease. OBJECTIVE: The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet. METHOD: The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO). METHOD: The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.

A review of big data technology and its application in cancer care.

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

Exploring the Potential of DNA Computing for Complex Big Data Problems: A Case Study on the Traveling Car Renter Problem.

The traveling car renter problem (TCRP) is a variant of the Traveling Salesman Problem (TSP) wherein the salesman utilizes rented cars for travel. The primary objective of this problem is to identify a solution that minimizes the cumulative operating costs. Given its classification as a non-deterministic polynomial (NP) problem, traditional computers are not proficient in effectively resolving it. Conversely, DNA computing exhibits unparalleled advantages when confronted with NP-hard problems. This paper presents a DNA algorithm, based on the Adleman-Lipton model, as a proposed approach to address TCRP. The solution for TCRP can be acquired by following a series of fundamental steps, including coding, interaction, and extraction. The time computing complexity of the proposed DNA algorithm is O(n2m) for TCRP with n cities and m types of cars. By conducting simulation experiments, the solutions for certain instances of TCRP are computed and compared to those obtained by alternative algorithms. The proposed algorithm further illustrates the potential of DNA computing, as a form of parallel computing, to address more intricate large-scale problems.

Big Data-Planetary Health approach for evaluating the Brazilian Dengue Control Program.

OBJECTIVE: This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. METHODS: Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. OUTCOMES: Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. CONCLUSIONS: This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location.

Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach.

BACKGROUND: Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity. METHODS: To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-h temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011-2012 data collected from the noninstitutionalized civilian resident population of the United States. The number of participants that had the whole set of physical activity data collected by the accelerometer is 6613. Among 6613 participants, 4242 participants had complete demography and Patient Health Questionnaire-9 (PHQ-9) questionnaire, a tool to quantify depressive symptoms. The association between activity-count behaviors and depressive symptoms was analyzed using multivariable logistic regression to adjust for confounding factors in sequential models. RESULTS: We identified four physical activity-count behaviors based on five physical activity-counting patterns classified by unsupervised machine learning. Regarding PHQ-9 scores, we found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group. CONCLUSIONS: Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability of assessing depressive symptoms effectively.

Big data analysis for Covid-19 in hospital information systems.

The COVID-19 pandemic has triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.

Research collaboration data platform ensuring general data protection.

Translational data is of paramount importance for medical research and clinical innovation. It has the potential to benefit individuals and organizations, however, the protection of personal data must be guaranteed. Collecting diverse omics data and electronic health records (EHR), re-using the minimized data, as well as providing a reliable data transfer between different institutions are mandatory steps for the development of the promising field of big data and artificial intelligence in medical research. This is made possible within the proposed data platform in this research project. The established data platform enables the collaboration between public and commercial organizations by data transfer from various clinical systems into a cloud for supporting multi-site research while ensuring compliant data governance.

Research to Improve Clinical Care in Family Medicine: Big Data, Telehealth, Artificial Intelligence, and More.

This issue highlights changes in medical care delivery since the start of the COVID-19 pandemic and features research to advance the delivery of primary care. Several articles report on the effectiveness of telehealth, including its use for hospital follow-up, medication abortion, management of diabetes, and as a potential tool for reducing health disparities. Other articles detail innovations in clinical practice, from the use of artificial intelligence and machine learning to a validated simple risk score that can support outpatient triage decisions for patients with COVID-19. Notably one article reports the impact of a voluntary program using scribes in a large health system on physician documentation behaviors and performance. One article addresses the wage gap between early-career female and male family physicians. Several articles report on inappropriate testing for common health problems; are you following recommendations for ordering Pulmonary Function Tests, mt-sDNA for colon cancer screening, and HIV testing?

Identification and classification of urban employment centers based on big data: A case study of Beijing.

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

BACKGROUND: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. OBJECTIVE: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. METHODS: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. RESULTS: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. CONCLUSIONS: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS).

Selection bias poses a substantial challenge to valid statistical inference in nonprobability samples. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large nonprobability sample, the COVID-19 Trends and Impact Survey (CTIS), and a small probability survey, the Center for Voting Options and Trends in Election Research (CVoter), against national benchmark data from the COVID Vaccine Intelligence Network. Notably, CTIS exhibits a larger estimation error on average (0.37) compared to CVoter (0.14). Additionally, we explored the accuracy (regarding mean squared error) of CTIS in estimating successive differences (over time) and subgroup differences (for females versus males) in mean vaccine uptakes. Compared to the overall vaccination rates, targeting these alternative estimands comparing differences or relative differences in two means increased the effective sample size. These results suggest that the Big Data Paradox can manifest in countries beyond the United States and may not apply equally to every estimand of interest.

Rapid determination of seismic influence field based on mobile communication big data-A case study of the Luding Ms 6.8 earthquake in Sichuan, China.

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.