Search results for: multivariate failure-time data

Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

Procedia PDF Downloads 161

Authors: Benny Sand, Yotam Lurie, Shlomo Mark

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Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

Procedia PDF Downloads 102

Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: F. Amin, S. Feizi

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Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

Procedia PDF Downloads 360

Authors: Iris W. A. Boot, Anke Wesselius, Maurice P. Zeegers

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Diet may play an essential role in the aetiology of bladder cancer (BC). Vitamin D is involved in various biological functions which have the potential to prevent BC development. Besides, vitamin D also influences the uptake of calcium and phosphorus , thereby possibly indirectly influencing the risk of BC. The aim of the present study was to investigate the relation between vitamin D intake and BC risk. Individual dietary data were pooled from three cohort studies. Food item intake was converted to daily intakes of vitamin D, calcium and phosphorus. Pooled multivariate hazard ratios (HRs), with corresponding 95% confidence intervals (CIs) were obtained using Cox-regression models. Analyses were adjusted for gender, age and smoking status (Model 1), and additionally for the food groups fruit, vegetables and meat (Model 2). Dose–response relationships (Model 1) were examined using a nonparametric test for trend. In total, 2,871 cases and 522,364 non-cases were included in the analyses. The present study showed an overall increased BC risk for high dietary vitamin D intake (HR: 1.14, 95% CI: 1.03-1.26). A similar increase BC risk with high vitamin D intake was observed among women and for the non-muscle invasive BC subtype, (HR: 1.41, 95% CI: 1.15-1.72, HR: 1.13, 95% CI: 1.01-1.27, respectively). High calcium intake decreased the BC risk among women (HR: 0.81, 95% CI: 0.67-0.97). A combined inverse effect on BC risk was observed for low vitamin D intake and high calcium intake (HR: 0.67, 95% CI: 0.48-0.93), while a positive effect was observed for high vitamin D intake in combination with low, moderate and high phosphorus (HR: 1.31, 95% CI: 1.09-1.59, HR: 1.17, 95% CI: 1.01-1.36, HR: 1.16, 95% CI: 1.03-1.31, respectively). Combining all nutrients showed a decreased BC risk for low vitamin D intake, high calcium and moderate phosphor intake (HR: 0.37, 95% CI: 0.18-0.75), and an increased BC risk for moderate intake of all the nutrients (HR: 1.18, 95% CI: 1.02-1.38), for high vitamin D and low calcium and phosphor intake (HR: 1.28, 95% CI: 1.01-1.62), and for moderate vitamin D and calcium and high phosphorus intake (HR: 1.27, 95% CI: 1.01-1.59). No significant dose-response analyses were observed. The findings of this study show an increased BC risk for high dietary vitamin D intake and a decreased risk for high calcium intake. Besides, the study highlights the importance of examining the effect of a nutrient in combination with complementary nutrients for risk assessment. Future research should focus on nutrients in a wider context and in nutritional patterns.

Keywords: bladder cancer, nutritional oncology, pooled cohort analysis, vitamin D

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Authors: Apkar Salatian

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To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

Procedia PDF Downloads 212

Authors: Nadia Naseem, Farwa Batool, Nasir Mehmood, AbdulHannan Nagi

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Background: The incidence and mortality of breast cancer vary significantly in geographically distinct populations. In Pakistan, breast cancer has shown an increase in incidence in young females and is characterized by more aggressive behavior. The tumor suppressor TP53 gene is a crucial genetic factor that plays a significant role in breast carcinogenesis. This study investigated the TP53 mutations in molecular subtypes of both nodes negative and positive breast cancer in young Pakistani patients. Material and Methods: p53, Estrogen Receptor (ER), Progesterone Receptor (PR), Her-2 neu and Ki 67 expressions were analyzed immunohistochemically in a series of 75 node negative (A) and 75 node positive (B) young (aged: 19-40 years) breast cancer patients diagnosed between 2014 to 2017 at two leading hospitals of Punjab, Pakistan. Tumor tissue specimens and peripheral blood samples were examined for TP53 mutations by direct sequencing of the gene (exons 4-9). The relation of TP53 mutations to these markers and clinicopathological data was investigated. Results: Mean age of the patients was 32.4 + 9.1 SD. Invasive breast carcinoma was the most frequent histological variant (A=92%, B=94.6%). Grade 3 carcinoma was the commonest grade (A=72%, B=81.3%). Triple negative cases (ER-, PR-, Her-2) formed most of the molecular subtypes (A=44%, B=50.6%). A total of 17.2% (A: 6.6%, B: 10.6%) patients showed TP53 mutations. Mutations were significantly more frequent in triple negative cases (A: 74.8%, B: 62.2%) compared to HER2-positive patients (P < 0.0001). In the multivariate analysis of the whole patient group, the independent prognosticator were triple negative cases (P=0.021), TP53 overexpression by IHC (P=0.001) and advanced-stage disease (P=0.007). No statistically significant correlation between TP53 mutations and clinicopathological parameters was found (P < 0.05). Conclusions: It is concluded that TP53 mutations are infrequently present in breast carcinoma of young Pakistani population and there was no significant correlation between p53 mutation and early onset disease. Immunohistochemically detected TP53 expression in our resource-constrained to set up can be beneficial in predicting mutations at the younger age in our population.

Keywords: immunohistochemistry (IHC), invasive breast carcinoma (IBC), Pakistan, TP53

Procedia PDF Downloads 158

Authors: Mario M. Figueroa de la Cruz, Claudia I. Solorzano, Raul Acosta, Ignacio Funes

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This technological development project seeks to create a tool that allows companies, in need of implementing a Data Center, intelligently determining factors for allocating resources support cooling and power supply (UPS) in its conception. The results should show clearly the speed, robustness and reliability of a system designed for deployment in environments where they must manage and protect large volumes of data.

Keywords: telecommunications, data center, fuzzy logic, expert systems

Procedia PDF Downloads 345

Authors: Ubaidillah Zuhdi, Shunsuke Mori, Kazuhisa Kamegai

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The purpose of this study is to forecast the influences of Information and Communication Technology (ICT) on the structural changes of Japanese economies based on Leontief Input-Output (IO) coefficients. This study establishes a statistical analysis to predict the future interrelationships among industries. We employ the Constrained Multivariate Regression (CMR) model to analyze the historical changes of input-output coefficients. Statistical significance of the model is then tested by Likelihood Ratio Test (LRT). In our model, ICT is represented by two explanatory variables, i.e. computers (including main parts and accessories) and telecommunications equipment. A previous study, which analyzed the influences of these variables on the structural changes of Japanese industrial sectors from 1985-2005, concluded that these variables had significant influences on the changes in the business circumstances of Japanese commerce, business services and office supplies, and personal services sectors. The projected future Japanese economic structure based on the above forecast generates the differentiated direct and indirect outcomes of ICT penetration.

Keywords: forecast, ICT, industrial structural changes, statistical analysis

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Authors: Amy Gooden, Meshandren Naidoo

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Genetic research is not confined to a particular jurisdiction. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.

Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa

Procedia PDF Downloads 161

Authors: Seema Dhariwal, Kiran Maan, Ruchi Baghel, Apoorva Sharma, Poonam Rana

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Introduction: Traumatic brain injury (TBI) is defined as the temporary or permanent alteration in brain function and pathology caused by an external mechanical force. It represents the leading cause of mortality and morbidity among children and youth individuals. Various models of TBI in rodents have been developed in the laboratory to mimic the scenario of injury. Blast overpressure injury is common among civilians and military personnel, followed by accidents or explosive devices. In addition to this, the lateral Controlled cortical impact (CCI) model mimics the blunt, penetrating injury. Method: In the present study, we have developed two different mild TBI models using blast and CCI injury. In the blast model, helium gas was used to create an overpressure of 130 kPa (±5) via a shock tube, and CCI injury was induced with an impact depth of 1.5mm to create diffusive and focal injury, respectively. C57BL/6J male mice (10-12 weeks) were divided into three groups: (1) control, (2) Blast treated, (3) CCI treated, and were exposed to different injury models. Serum was collected on Day1 and day7, followed by biphasic extraction using MTBE/Methanol/Water. Prepared samples were separated on Charged Surface Hybrid (CSH) C18 column and acquired on UHPLC-Q-TOF-MS using ESI probe with inhouse optimized parameters and method. MS peak list was generated using Markerview TM. Data were normalized, Pareto-scaled, and log-transformed, followed by multivariate and univariate analysis in metaboanalyst. Result and discussion: Untargeted profiling of lipids generated extensive data features, which were annotated through LIPID MAPS® based on their m/z and were further confirmed based on their fragment pattern by LipidBlast. There is the final annotation of 269 features in the positive and 182 features in the negative mode of ionization. PCA and PLS-DA score plots showed clear segregation of injury groups to controls. Among various lipids in mild blast and CCI, five lipids (Glycerophospholipids {PC 30:2, PE O-33:3, PG 28:3;O3 and PS 36:1 } and fatty acyl { FA 21:3;O2}) were significantly altered in both injury groups at Day 1 and Day 7, and also had VIP score >1. Pathway analysis by Biopan has also shown hampered synthesis of Glycerolipids and Glycerophospholipiods, which coincides with earlier reports. It could be a direct result of alteration in the Acetylcholine signaling pathway in response to TBI. Understanding the role of a specific class of lipid metabolism, regulation and transport could be beneficial to TBI research since it could provide new targets and determine the best therapeutic intervention. This study demonstrates the potential lipid biomarkers which can be used for injury severity diagnosis and identification irrespective of injury type (diffusive or focal).

Keywords: LipidBlast, lipidomic biomarker, LIPID MAPS®, TBI

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Authors: Baris Can Yalcin

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Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor.

Keywords: design, mechatronics, motion sensor, data acquisition

Procedia PDF Downloads 588

Authors: Tarek Elnimr, Reda Morsy, Assem El Fert, Aziza Ismail

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Hepatitis is an inflammation of the liver. The condition can be self-limiting or can progress to fibrosis, cirrhosis or liver cancer. Disease caused by the hepatitis virus, the virus can cause hepatitis infection, ranging in severity from a mild illness lasting a few weeks to a serious, lifelong illness. A growing body of evidence indicates that many trace elements play important roles in a number of carcinogenic processes that proceed with various mechanisms. To examine the status of trace elements during the development of hepatic carcinoma, we determined the iron, copper, zinc and selenium levels in some biological samples of patients at different stages of viral hepatic disease. We observed significant changes in the iron, copper, zinc and selenium levels in the biological samples of patients hepatocellular carcinoma, relative to those of healthy controls. The mean hair, nail, RBC, serum and whole blood copper levels in patients with hepatitis virus were significantly higher than that of the control group. In contrast the mean iron, zinc, and selenium levels in patients having hepatitis virus were significantly lower than those of the control group. On the basis of this study, we identified the impact of natural supplements to improve the treatment of viral liver damage, using the level of some trace elements such as, iron, copper, zinc and selenium, which might serve as biomarkers for increases survival and reduces disease progression. Most of the elements revealed diverse and random distribution in the samples of the donor groups. The correlation study pointed out significant disparities in the mutual relationships among the trace elements in the patients and controls. Principal component analysis and cluster analysis of the element data manifested diverse apportionment of the selected elements in the scalp hair, nail and blood components of the patients compared with the healthy counterparts.

Keywords: hepatitis, hair, nail, blood components, trace element, nutrition therapy, multivariate analysis, correlation, ICP-MS

Procedia PDF Downloads 408

Authors: Rute Faria, Digo Moraes, Amanda Santos, Dione Morais, Maria Sartori

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Seed vigor is a fundamental component for the good performance of the entire soybean production process. Seeds with mechanical damage at harvest time will be more susceptible to fungal and insect attack during storage, which will invariably reduce their vigor to the field, compromising uniformity and final stand performance. Harvesters, even the most modern ones, when not properly regulated or operated, can cause irreversible damages to the seeds, compromising even their commercialization. Therefore, the control of an efficient harvest is necessary in order to guarantee a good quality final product. In this work, the damage caused by two different harvesters (one rented, and another one) was evaluated, traveling in two speeds (4 and 8 km / h). The design was completely randomized in 2 x 2 factorial, with four replications. To evaluate the physiological quality seed germination and vigor tests were carried out over a period of six months. A multivariate analysis of Principal Components (PCA) and clustering allowed us to verify that the leased machine had better performance in the incidence of immediate damages in the seeds, but after a storage period of 6 months the vigor of these seeds reduced more than own machine evidencing that such a machine would bring more damages to the seeds.

Keywords: Glycine max (L.), cluster analysis, PCA, vigor

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Authors: Ashish Dhamaniya, Satish Chandra

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Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85^th and 50^th percentile speed to the difference in 50^th and 15^th percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also.

Keywords: normal distribution, percentile speed, speed spread ratio, traffic volume

Procedia PDF Downloads 422

Authors: Ming Wei

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Over the past two decades, Location Based Service (LBS) data have been increasingly applied to urban and transportation studies due to their comprehensiveness and consistency. However, compared to other LBS data including mobile phone data, GPS and social networking platforms, smart card data collected from public transport users have arguably yet to be fully exploited in urban systems analysis. By using five weekdays of passenger travel transaction data taken from go card – Southeast Queensland’s transit smart card – this paper analyses the spatiotemporal distribution of passenger movement with regard to the land use patterns in Brisbane. Work and residential places for public transport commuters were identified after extracting journeys-to-work patterns. Our results show that the locations of the workplaces identified from the go card data and residential suburbs are largely consistent with those that were marked in the land use map. However, the intensity for some residential locations in terms of population or commuter densities do not match well between the map and those derived from the go card data. This indicates that the misalignment between residential areas and workplaces to a certain extent, shedding light on how enhancements to service management and infrastructure expansion might be undertaken.

Keywords: big data, smart card data, travel pattern, land use

Procedia PDF Downloads 285

Authors: A. Abiodun, G. George, B. Longo-Mbenza, E. Blanco-Blanco

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Objective: To determine the prevalence and determinants of iron-deficiency anaemia in pregnant Xhosa women practising geophagia. Methods: This cross-sectional study was conducted among pregnant Xhosa women from rural areas of Mthatha, South Africa, according to socio-demographic, geophagia, haematologic and iron metabolism profiles using univariate and multivariate analyses. Anaemia was defined by haemoglobin <11 g/dL and iron deficiency was defined by serum ferritin < 12 ug/L. Results: Out of 210 pregnant women (mean age =23±5.3 for geophagic and 25.6±5.3 for non-geophagic), 51.4% (n = 108) had iron deficiency anaemia (50.9% geophagic and 49.1% non-geophagic). After adjusting for confounders, only geophagia (OR=2.1 95% CI 1.1-4.2; P=0.029) and mean corpuscular haemoglobin concentration categories (< 30.5 g/dL with OR=16.6 95% CI 6.8-40.2; P < 0.0001; 30.5-31.5 g/dL with OR=2.9 95% CI 1.4-6.1; P=0.006; and ≥ 31.5 g/dL with OR=1) were identified as the most important significant and independent determinants of iron deficiency anaemia. Conclusion: The study results point to the potential harm geophagia can cause in pregnant women. The prevalence of iron deficiency anaemia is unacceptably high. Geophagic behaviour, low MCHC presented as particular risk factors of iron deficiency anaemia in this study. Education and counselling about appropriate diet during pregnancy and prevention of geophagic behaviour (and health consequences) are needed among pregnant Xhosa women.

Keywords: geophagia, pregnancy, iron deficiency anaemia, Xhosa

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: die-map clustering, feature extraction, pattern recognition, semiconductor manufacturing process

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Authors: Afiq Juazer Rizal, Siti Zaleha Misnan, M. Zairi M. Yusof

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Seismic data is the fundamental tool utilized by exploration companies to determine potential hydrocarbon. However, the importance of seismic trace data will be undermined unless the geo-spatial component of the data is understood. Deriving a proposed well to be drilled from data that has positional ambiguity will jeopardize business decision and millions of dollars’ investment that every oil and gas company would like to avoid. Spatial integrity QC workflow has been introduced in PETRONAS to ensure positional errors within the seismic data are recognized throughout the exploration’s lifecycle from acquisition, processing, and seismic interpretation. This includes, amongst other tests, quantifying that the data is referenced to the appropriate coordinate reference system, survey configuration validation, and geometry loading verification. The direct outcome of the workflow implementation helps improve reliability and integrity of sub-surface geological model produced by geoscientist and provide important input to potential hazard assessment where positional accuracy is crucial. This workflow’s development initiative is part of a bigger geospatial integrity management effort, whereby nearly eighty percent of the oil and gas data are location-dependent.

Keywords: oil and gas exploration, PETRONAS, seismic data, spatial integrity QC workflow

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Authors: Maryam Aloshan, Imam Mohammad Ibn Saud, Ahmad Khudair

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This study assesses the data governance maturity of nonprofit organizations in Riyadh, Saudi Arabia, using the National Data Maturity Index (NDI) framework developed by the Saudi Data and Artificial Intelligence Authority (SDAIA). Employing a survey designed around the NDI model, data maturity levels were evaluated across 14 dimensions using a 5-point Likert scale. The results reveal a spectrum of maturity levels among the organizations surveyed: while some medium-sized associations reached the ‘Defined’ stage, others, including large associations, fell within the ‘Absence of Capabilities’ or ‘Building’ phases, with no organizations achieving the advanced ‘Established’ or ‘Pioneering’ levels. This variation suggests an emerging recognition of data governance but underscores the need for targeted interventions to bridge the maturity gap. The findings point to a significant opportunity to elevate data governance capabilities in Saudi nonprofits through customized capacity-building initiatives, including training, mentorship, and best practice sharing. This study contributes valuable insights into the digital transformation journey of the Saudi nonprofit sector, aligning with national goals for data-driven governance and organizational efficiency.

Keywords: nonprofit organizations-national data maturity index (NDI), Saudi Arabia- SDAIA, data governance, data maturity

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Authors: Florian Tille, Heide Weishaar, Bernhard Gibis, Susanne Schnitzer

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Introduction: The quality of patient-doctor communication and of written health information is central to organizational health literacy (HL). Whether patients understand their doctors’ explanations and textual material on health, however, is understudied. This study identifies the overall levels of patient understanding of health information and its associations with patients’ social characteristics in outpatient health care in Germany. Materials & Methods: This analysis draws on data collected via a 2017 national health survey with a sample of 6,105 adults. Quality of communication was measured for consultations with general practitioners (GPs) and specialists (SPs) via the Ask Me 3 program questions, and through a question on written health material. Correlations with social characteristics were explored employing bivariate and multivariate logistic regression analyses. Results: Over 90% of all respondents reported that they had understood their doctors’ explanations during the last consultation. Failed understanding was strongly correlated with patients’ very poor health (Odds Ratio [OR]: 5.19; 95% confidence interval [CI]: 2.23–12.10; ref. excellent/very good health), current health problem (OR: 6.54, CI: 1.70–25.12; ref. preventive examination) and age 65 years and above (OR: 2.97, CI: 1.10–8.00; ref. 18 to 34 years). Fewer patients answered they understood written material well (86.7% for las visit at GP, 89.7% at SP). Understanding written material poorly was highly associated with basic education (OR: 4.20, CI: 2.76–6.39; ref. higher education) and 65 years old and above (OR: 2.66, CI: 1.43–4.96). Discussion: Overall ratings of oral patient-doctor communication and written communication of health information are high. Yet, a considerable share of patients reports not-understanding their doctors and poor understanding of the written health-related material. Interventions that can contribute to improving organizational HL in outpatient care in Germany include HL training for doctors, reducing system barriers to easily-accessible health information for patients and combining oral and written health communication means. Conclusion: This work adds to the study of organizational HL in Germany. To increase patient understanding of health-relevant information and thereby possibly reduce health disparities, meeting the communication needs especially of persons in different age groups, with basic education and in very poor health is suggested.

Keywords: health survey, organizational health literacy, patient-doctor communication, social characteristics, outpatient care, Ask Me 3

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Authors: Zhenqiu Liu

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Visualizing the heterogeneity within cell-populations for single-cell RNA-seq data is crucial for studying the functional diversity of a cell. However, because of the high level of noises, outlier, and dropouts, it is very challenging to measure the cell-to-cell similarity (distance), visualize and cluster the data in a low-dimension. Minimum volume embedding (MVE) projects the data into a lower-dimensional space and is a promising tool for data visualization. However, it is computationally inefficient to solve a semi-definite programming (SDP) when the sample size is large. Therefore, it is not applicable to single-cell RNA-seq data with thousands of samples. In this paper, we develop an efficient algorithm with an accelerated proximal gradient method and visualize the single-cell RNA-seq data efficiently. We demonstrate that the proposed approach separates known subpopulations more accurately in single-cell data sets than other existing dimension reduction methods.

Keywords: single-cell RNA-seq, minimum volume embedding, visualization, accelerated proximal gradient method

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Authors: Sara Ibn El Ahrache, Tajje-eddine Rachidi, Hassan Badir, Abderrahmane Sbihi

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Recently, there has been increasing confidence for a favorable usage of big data drawn out from the huge amount of information deposited in a cloud computing system. Data kept on such systems can be retrieved through the network at the user’s convenience. However, the data that users send include private information, and therefore, information leakage from these data is now a major social problem. The usage of secret sharing schemes for cloud computing have lately been approved to be relevant in which users deal out their data to several servers. Notably, in a (k,n) threshold scheme, data security is assured if and only if all through the whole life of the secret the opponent cannot compromise more than k of the n servers. In fact, a number of secret sharing algorithms have been suggested to deal with these security issues. In this paper, we present a Mapreduce implementation of Shamir’s secret sharing scheme to increase its performance and to achieve optimal security for cloud data. Different tests were run and through it has been demonstrated the contributions of the proposed approach. These contributions are quite considerable in terms of both security and performance.

Keywords: cloud computing, data security, Mapreduce, Shamir's secret sharing

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Authors: Kyle De Freitas, Margaret Bernard

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Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

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Authors: Vikrum Malhotra, Manpreet Kaur, Ayesha Ghotto

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Background: Cardiac registries such as ACC Percutaneous Coronary Intervention Registry require high quality data to be abstracted, including data elements such as nuclear cardiology, diagnostic coronary angiography, and PCI. Introduction: The audit tool created is used by data abstractors to provide data audits and assess the accuracy and inter-rater reliability of abstraction performed by the abstractors for a health system. This audit tool solution has been developed across 13 registries, including ACC/NCDR registries, PCI, STS, Get with the Guidelines. Methodology: The data audit tool was used to audit internal registry abstraction for all data elements, including stress test performed, type of stress test, data of stress test, results of stress test, risk/extent of ischemia, diagnostic catheterization detail, and PCI data elements for ACC/NCDR PCI registries. This is being used across 20 hospital systems internally and providing abstraction and audit services for them. Results: The data audit tool had inter-rater reliability and accuracy greater than 95% data accuracy and IRR score for the PCI registry in 50 PCI registry cases in 2021. Conclusion: The tool is being used internally for surgical societies and across hospital systems. The audit tool enables the abstractor to be assessed by an external abstractor and includes all of the data dictionary fields for each registry.

Keywords: abstraction, cardiac registry, cardiovascular registry, registry, data

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Authors: Seyed Esmail Seyedi Bariran, Laysheng Ewe, Amy Ling

Abstract:

Since supplier selection appears as a vital decision, selecting supplier based on the best and most accurate ways has a lot of importance for enterprises. In this study, a new Artificial Intelligence approach is exerted to remove weaknesses of supplier selection. The paper has three parts. First part is choosing the appropriate criteria for assessing the suppliers’ performance. Next one is collecting the data set based on experts. Afterwards, the data set is divided into two parts, the training data set and the testing data set. By the training data set the best structure of GEP and ANN are selected and to evaluate the power of the mentioned methods the testing data set is used. The result obtained shows that the accuracy of GEP is more than ANN. Moreover, unlike ANN, a mathematical equation is presented by GEP for the supplier selection.

Keywords: supplier selection, automotive supply chains, ANN, GEP

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Authors: Yasuyuki Takahashi, Maya Yamashita, Kyoko Saito

Abstract:

In general, dynamic SPECT data acquisition needs a few minutes for one rotation. Thus, the time-activity curve (TAC) derived from the dynamic SPECT is relatively coarse. In order to effectively shorten the interval, between data points, we adopted a 180-degree interpolation method. This method is already used for reconstruction of the X-ray CT data. In this study, we applied this 180-degree interpolation method to SPECT and investigated its effectiveness.To briefly describe the 180-degree interpolation method: the 180-degree data in the second half of one rotation are combined with the 180-degree data in the first half of the next rotation to generate a 360-degree data set appropriate for the time halfway between the first and second rotations. In both a phantom and a patient study, the data points from the interpolated images fell in good agreement with the data points tracking the accumulation of 99mTc activity over time for appropriate region of interest. We conclude that data derived from interpolated images improves the apparent time resolution of dynamic SPECT.

Keywords: dynamic SPECT, time resolution, 180-degree interpolation method, 99mTc-GSA.

Procedia PDF Downloads 493

Authors: Rim Sghiri, Samia Al Shouli, Hana Benhassine, Nejla Elamri, Zahid Shakoor, Foued Slama, Adel Almogren, Hala Zeglaoui, Elyes Bouajina, Ramzi Zemni

Abstract:

Objectives: Little is known about genes predisposing to systemic bone loss (SBL) in rheumatoid arthritis (RA). Therefore, we examined the association between SBL and a variant of CD40 gene, which is known to play a critical role in both immune response and bone homeostasis among patients with RA. Methods: CD40 rs48104850 was genotyped in 176 adult RA patients. Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA). Results: Low BMD was observed in 116 (65.9%) patients. Among them, 60 (34.1%) had low femoral neck (FN) Z score, 72 (40.9%) had low total femur (TF) Z score, and 105 (59.6%) had low lumbar spine (LS) Z score. CD40 rs4810485 was found to be associated with reduced TF Z score with the CD40 rs4810485 T allele protecting against reduced TF Z score (OR = 0.40, 95% CI = 0.23-0.68, p = 0.0005). This association was confirmed in the multivariate logistic regression analysis (OR=0.31, 95% CI= 0.16-0.59, p=3.84 x 10₋₄). Moreover, median FN BMD was reduced among RA patients with CD40 rs4810485 GG genotype compared to RA patients harbouring CD40 rs4810485 TT and GT genotypes (0.788± 0.136 versus 0.826± 0.146g/cm², p=0.001). Conclusion: This study, for the first time ever, demonstrated an association between a CD40 genetic variant and SBL among patients with RA.

Keywords: rheumatoid arthritis, CD40 gene, bone mineral density, systemic bone loss, rs48104850

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Authors: Gwenyth C. Eichfeld

Abstract:

Variation in global COVID-19 spread is partly explained by social and behavioral factors. Many of these behaviors are linked to genetics. The short polymorphism of the 5-HTTLPR promoter region of the SLC6A4 gene is linked to collectivism. The seven-repeat polymorphism of the DRD4 gene is linked to risk-taking, migration, sensation-seeking, and impulsivity. Fewer CAG repeats in the androgen receptor gene are linked to impulsivity. This study investigates an association between the country-level frequency of these variants and early Covid-19 spread. Results of regression analysis indicate a significant association between increased country-wide prevalence of the short allele of the SLC6A4 gene and decreased COVID-19 spread when other factors that have been linked to COVID-19 are controlled for. Additionally, results show that the short allele of the SLC6A4 gene is associated with COVID-19 spread through GDP and percent urbanization rather than collectivism. Results showed no significant association between the frequency of the DRD4 polymorphism nor the androgen receptor polymorphism with early COVID-19 spread.

Keywords: neuroscience, genetics, population sciences, Covid-19

Procedia PDF Downloads 36