Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace.

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

An Evaluation of Impostor Phenomenon in Data Science Students.

Impostor Phenomenon (IP), also called impostor syndrome, involves feelings of perceived fraudulence, self-doubt, and personal incompetence that persist despite one's education, experience, and accomplishments. This study is the first to evaluate the presence of IP among data science students and to evaluate several variables linked to IP simultaneously in a single study evaluating data science. In addition, it is the first study to evaluate the extent to which gender identification is linked to IP. We examined: (1) the degree to which IP exists in our sample; (2) how gender identification is linked to IP; (3) whether there are differences in goal orientation, domain identification, perfectionism, self-efficacy, anxiety, personal relevance, expectancy, and value for different levels of IP; and (4) the extent to which goal orientation, domain identification, perfectionism, self-efficacy, anxiety, personal relevance, expectancy, and value predict IP. We found that most students in the sample showed moderate and frequent levels of IP. Moreover, gender identification was positively related to IP for both males and females. Finally, results indicated significant differences in perfectionism, value, self-efficacy, anxiety, and avoidance goals by IP level and that perfectionism, self-efficacy, and anxiety were particularly noteworthy in predicting IP. Implications of our findings for improving IP among data science students are discussed.

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE: We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS: PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS: In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS: The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION: PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.

Clinical profile, demographic distribution, and outcomes of ocular siderosis: Electronic medical record-driven big data analytics from an eye care network in India.

Purpose: To describe the demographics, clinical profile, and outcomes of ocular siderosis in patients presenting to a multi-tier ophthalmology hospital network in India. Methods: This cross-sectional and hospital-based study included 3,082,727 new patients who presented between August 2010 and December 2021. Patients with a clinical diagnosis of ocular siderosis in at least one eye were included. Results: Overall, 58 eyes of 57 patients (0.002%) were diagnosed with ocular siderosis. The majority were men (96.49%) and had unilateral (98.25%) affliction. The most common age group at presentation was during the third decade of life with 24 patients (42.11%). A clear history of ocular trauma was documented in 47 patients (81.03%). Major clinical signs included corneal pigment deposition in nearly half of the eyes (27/58 eyes, 46.55%), corneal scar (20/58 eyes, 34.48%), cataract (22/58 eyes, 37.93%) and retinal detachment (11/58 eyes, 18.96%). The intraocular foreign body (IOFB) was anatomically localized in a majority of the eyes (i.e., 45/58 eyes, 77.59%). The most common location of the IOFB was in the posterior segment (22/58 eyes, 37.93%). The eyes that underwent a vitreoretinal surgery with removal of IOFB had a slightly better BCVA (1.0 ± 1.01) when compared to eyes with non-removal of IOFB (1.58 ± 1.00). Conclusion: Ocular siderosis is a rare sight-threatening entity, with half of the affected eyes exhibiting severe visual impairment. Majority of the eyes in ocular siderosis will have a detectable IOFB. Surgical removal of IOFB may lead to a better visual gain when compared to non-removal.

Evaluation of data analytics workshop using RStudio amongst medical students in Pakistan.

A quasi-experimental study was conducted at the Aga Khan University, Karachi, Pakistan, to evaluate the outcomes of a series of workshops on 25 medical students' statistical knowledge and acceptance of RStudio. The knowledge in each of the five sessions was assessed using pre- and post- knowledge-based quizzes. In addition, the Student's Attitude Towards Statistics (SATS-36) and the Technology Acceptance Model were used. Data analysis on RStudio revealed a statistically significant improvement in knowledge in all five sessions (p<0.05). SATS-36 showed statistically significant improvement in Cognitive Competence (p<0.001). RStudio had commendable acceptance with relatively high scores of Attitudes (behavioural intention, median = 6.00 [5.20-7.00]) and Utility (perceived usefulness, median = 5.20 [4.10-6.20]). In conclusion, medical students had improved statistical knowledge and acceptance towards the novel statistical tool. Hence, further studies must evaluate the effectiveness of RStudio when integrated as part of the medical curriculum.

How data science and AI-based technologies impact genomics.

Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.

Evaluation of AIML + HDR-A Course to Enhance Data Science Workforce Capacity for Hispanic Biomedical Researchers.

Artificial intelligence (AI) and machine learning (ML) facilitate the creation of revolutionary medical techniques. Unfortunately, biases in current AI and ML approaches are perpetuating minority health inequity. One of the strategies to solve this problem is training a diverse workforce. For this reason, we created the course "Artificial Intelligence and Machine Learning applied to Health Disparities Research (AIML + HDR)" which applied general Data Science (DS) approaches to health disparities research with an emphasis on Hispanic populations. Some technical topics covered included the Jupyter Notebook Framework, coding with R and Python to manipulate data, and ML libraries to create predictive models. Some health disparities topics covered included Electronic Health Records, Social Determinants of Health, and Bias in Data. As a result, the course was taught to 34 selected Hispanic participants and evaluated by a survey on a Likert scale (0-4). The surveys showed high satisfaction (more than 80% of participants agreed) regarding the course organization, activities, and covered topics. The students strongly agreed that the activities were relevant to the course and promoted their learning (3.71 ± 0.21). The students strongly agreed that the course was helpful for their professional development (3.76 ± 0.18). The open question was quantitatively analyzed and showed that seventy-five percent of the comments received from the participants confirmed their great satisfaction.

Cells as the first data scientists.

The concepts that we generally associate with the field of data science are strikingly descriptive of the way that life, in general, processes information about its environment. The 'information life cycle', which enumerates the stages of information treatment in data science endeavours, also captures the steps of data collection and handling in biological systems. Similarly, the 'data-information-knowledge ecosystem', developed to illuminate the role of informatics in translating raw data into knowledge, can be a framework for understanding how information is constantly being transferred between life and the environment. By placing the principles of data science in a broader biological context, we see the activities of data scientists as the latest development in life's ongoing journey to better understand and predict its environment. Finally, we propose that informatics frameworks can be used to understand the similarities and differences between abiotic complex evolving systems and life.

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture.

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

[Data Analytics: incidence of air pollution on public health in Medellin, Colombia]. / Analítica de datos: incidencia de la contaminación ambiental en la salud pública en Medellín (Colombia).

OBJECTIVE: To analyze the impact of air pollution by PM2,5 particulate matter and its relationship with the number of attendances to health entities for respiratory diseases through data analytics. METHODS: Data from the Metropolitan Area of Medellín, Colombia, a city located in a densely populated and industrialized narrow valley and that has presented critical episodes of contamination in recent years, were analyzed. Three data sources were analyzed: meteorological data provided by SIATA (Early Warning System of Medellín and the Aburra Valley), PM2,5 particulate matter contamination data provided by SIATA, and RIPS reports (Individual Registers for the Provision of Health Services) provided by the health department. RESULTS: The relationship between the concentration of PM2,5 and medical care for the diagnoses of ARI, COPD and asthma was evidenced. In a critical episode of PM2,5 contamination, the following delays in medical care were found: between 0-2 days for IRA, 0-7 days for COPD, and 0-5 days for asthma. DISCUSSION: Correlation coefficients were found that show the association of the concentration of PM2,5 with the attendances for the diagnoses of ARI, COPD, and asthma. The highest correlation between the three morbidities was found for asthma. The meteorological variable with the highest correlation with the objective variable is air temperature in the case of COPD and asthma. In the case of IRA, the variable with the highest correlation is wind speed. On the other hand, the day of the week is a variable of great importance when carrying out a study of care for diseases.

OBJETIVO: Analizar el impacto de la contaminación del aire por material particulado PM2,5 y su relación con el número de asistencias a entidades de salud por enfermedades respiratorias por medio de analítica de datos. MÉTODOS: Se analizaron datos del Área Metropolitana de Medellín, Colombia, ciudad ubicada en un valle estrecho densamente poblado e industrializado y que ha presentado episodios críticos de contaminación en los últimos años. Se analizaron tres fuentes de datos: datos meteorológicos aportados por el SIATA (Sistema de Alerta Temprana de Medellín y el Valle de Aburrá); datos de contaminación por material particulado PM2,5 aportados por SIATA; y reportes de los RIPS (Registros Individuales de Prestación de Servicios de Salud) aportados por la Secretaría de Salud. RESULTADOS: Se evidenció la relación entre la concentración de PM2,5 con las asistencias médicas por los diagnósticos de IRA, EPOC y asma. En un episodio crítico de contaminación por PM2,5, se encontraron los siguientes retardos en la atención médica: entre 0 y 2 días para el IRA, 0 y 7 días para el EPOC y 0 y 5 días para el asma. DISCUSIÓN: Se encontraron coeficientes de correlación que evidencian la asociación de la concentración de PM2,5 con las asistencias por los diagnósticos de IRA, EPOC y asma. La mayor correlación entre las tres morbilidades se presentó para el asma. La variable meteorológica de mayor correlación con la variable objetivo es la temperatura del aire para el caso de EPOC y asma. En el caso de IRA, la variable con mayor correlación es la velocidad del viento. Por otro lado, el día de la semana es una variable de gran importancia a la hora de realizar un estudio de atenciones por enfermedades.

[Drug discovery research using AI and data science: analyzing literature information to understand pathological mechanisms].

Recent rapid progress in big data and breakthrough AI technologies have brought about significant changes in the medical field as well. Although biomedical literature databases contain so many articles that it is impossible to read them all, AI technology based on neural networks has dramatically advanced and is now able to efficiently process such vast amounts of literature information in a short time. Since drug discovery research requires up-to-date and extensive knowledge of various disciplines, it is necessary to proactively incorporate AI technology to seamlessly obtain the information needed. In this article, we introduce our effort to use the rapidly growing literature data and the latest AI technologies to drug discovery research. Conventional search engines take an enormous amount of time to identify and understand sentences describing the subject matter of interest in the retrieved articles. We developed and validated our new search tool that not only has a conventional keyword search function, but also enables conceptual search for disease mechanisms using sentences. We will also describe problems that we have identified through actual use of the tool. Finally, since literature data is expected to increase and efforts to determine how to efficiently analyze and obtain desired findings using AI will become even more active, we will discuss expectations for future technological advances and issues that need to be resolved.

Measuring the Impact of a Blood Supply Shortage Using Data Science.

BACKGROUND: Transfusion medicine is the only section of the clinical laboratory that performs diagnostic testing and dispenses a drug (blood) on the basis of those results. However, not all of the testing that informs the clinical decision to prescribe a blood transfusion is performed in the blood bank. To form a holistic assessment of blood bank responsiveness to clinical needs, it is important to be able to merge blood bank data with datapoints from the hematology laboratory and the electronic medical record. METHODS: We built an interactive visualization of the time from hemoglobin result availability to initiation of red blood cell (RBC) transfusion and monitored the result over a 2-year period that coincided with several severe blood shortages. The visualization runs entirely on free software and was designed to be feasibly deployed on a variety of hospital information technology platforms without the need for significant data science expertise. RESULTS: Patient factors, such as hemoglobin concentration, blood type, and presence of minor blood group antibodies influenced the time to initiation of transfusion. Time to transfusion initiation did not appear to be significantly affected by periods of blood shortage. CONCLUSION: Overall, we demonstrate a proof of concept that complex, but clinically important, blood bank quality metrics can be generated with the support of a free, user-friendly system that aggregates data from multiple sources.

Data-driven techniques for temperature data prediction: big data analytics approach.

For extrapolation, climate change and other meteorological analysis, a study of past and current weather events is a prerequisite. NASA (National Aeronautics and Space Administration) has been able to develop a model capable of predicting various weather data for any location on the Earth, including locations lacking weather stations, weather satellite coverage, and other weather measuring instruments. This paper evaluates the prediction accuracy of the NASA temperature data with respect to NiMet (Nigerian Meteorological Agency) ground truth measurement, using Akwa Ibom Airport as a case study. Exploratory data analysis (descriptive and diagnostic analyses) of temperature retrieved from NiMet and NASA was performed to give a clear path to follow for predictive and prescriptive analyses. Using 2783 days of weather data retrieved from NiMet as ground truth, the accuracy of NASA predictions with the corresponding resolution was calculated. Mean absolute error (MAE) of 2.184 °C and root mean square error (RMSE) of 2.579 °C, with a coefficient of determination (R2) of 0.710 for maximum temperature, then MAE of 0.876 °C, RMSE of 1.225 °C with a coefficient of determination (R2) of 0.620 for minimum temperature was discovered. There is a good correlation between the two datasets; hence, a model can be developed to generate more accurate predictions, using the NASA data as input. Predictive and prescriptive analyses were performed by employing five prediction algorithms: decision tree regression, XGBoost regression and MLP (multilayer perceptron) with LBFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno) optimizer, MLP with SGD (stochastic gradient) optimizer and MLP with Adam optimizer. The MLP LBFGS algorithm performed best, by significantly reducing the MAE by 35.35% and RMSE by 31.06% for maximum temperature, accordingly, MAE by 10.05% and RMSE by 8.00% for minimum temperature. Results obtained show that given sufficient data, plugging NASA predictions as input to an LBFGS-MLP model gives more accurate temperature predictions for the study area.

Leveraging Clinical Informatics and Data Science to Improve Care and Facilitate Research in Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference.

OBJECTIVES: The use of electronic algorithms, clinical decision support systems, and other clinical informatics interventions is increasing in critical care. Pediatric acute respiratory distress syndrome (PARDS) is a complex, dynamic condition associated with large amounts of clinical data and frequent decisions at the bedside. Novel data-driven technologies that can help screen, prompt, and support clinician decision-making could have a significant impact on patient outcomes. We sought to identify and summarize relevant evidence related to clinical informatics interventions in both PARDS and adult respiratory distress syndrome (ARDS), for the second Pediatric Acute Lung Injury Consensus Conference. DATA SOURCES: MEDLINE (Ovid), Embase (Elsevier), and CINAHL Complete (EBSCOhost). STUDY SELECTION: We included studies of pediatric or adult critically ill patients with or at risk of ARDS that examined automated screening tools, electronic algorithms, or clinical decision support systems. DATA EXTRACTION: Title/abstract review, full text review, and data extraction using a standardized data extraction form. DATA SYNTHESIS: The Grading of Recommendations Assessment, Development and Evaluation approach was used to identify and summarize evidence and develop recommendations. Twenty-six studies were identified for full text extraction to address the Patient/Intervention/Comparator/Outcome questions, and 14 were used for the recommendations/statements. Two clinical recommendations were generated, related to the use of electronic screening tools and automated monitoring of compliance with best practice guidelines. Two research statements were generated, related to the development of multicenter data collaborations and the design of generalizable algorithms and electronic tools. One policy statement was generated, related to the provision of material and human resources by healthcare organizations to empower clinicians to develop clinical informatics interventions to improve the care of patients with PARDS. CONCLUSIONS: We present two clinical recommendations and three statements (two research one policy) for the use of electronic algorithms and clinical informatics tools for patients with PARDS based on a systematic review of the literature and expert consensus.

The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science.

Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general classifications of data, semantic segmentations of surgical instruments and low-volume weak annotations of specific abdominal organs. The Dresden Surgical Anatomy Dataset provides semantic segmentations of eight abdominal organs (colon, liver, pancreas, small intestine, spleen, stomach, ureter, vesicular glands), the abdominal wall and two vessel structures (inferior mesenteric artery, intestinal veins) in laparoscopic view. In total, this dataset comprises 13195 laparoscopic images. For each anatomical structure, we provide over a thousand images with pixel-wise segmentations. Annotations comprise semantic segmentations of single organs and one multi-organ-segmentation dataset including segments for all eleven anatomical structures. Moreover, we provide weak annotations of organ presence for every single image. This dataset markedly expands the horizon for surgical data science applications of computer vision in laparoscopic surgery and could thereby contribute to a reduction of risks and faster translation of Artificial Intelligence into surgical practice.

Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach.

Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824-0.877) and 0.84 (0.812-0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688-0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.