Generic Data Research Articles

Are Turkish pharmaceutical pricing strategies an access barrier to oncology medicines for Türkiye?

Objectives: Cancer diagnosis is increasing day by day all over the world. Deaths due to cancer are among the most common causes of death. Access to cancer drugs is a priority of health policies. The aim of this study is to evaluate access to cancer drugs through drug box sales data by modeling population growth, cancer incidence, and Fixed Euro Exchange (FEE) rate parameters used in drug pricing in Türkiye. Methods: Access to cancer drugs was evaluated by drug box sales figures obtained from IQVIA. Box sales data were classified according to diagnosis codes (ICD-10), reference, or generic status. Consumption of cancer drugs was examined over time with panel regression analysis, taking into account variables of population growth, cancer incidence, and the FEE rate in drug pricing in Türkiye. Results: The incidence of cancer in Türkiye was 215.1 in 2010 and 223.1 (per hundred thousand) in 2017. Whereas there was a 127.02% increase in the real euro exchange rate, there was an 89.6% increase in the FEE rate. With the regression approach, there is a negative relationship between the real and fixed exchange rate difference (RFED) and reference and generic drug consumption data. Medicine access is affected depending on diagnosis codes at different levels. Colorectal cancer medicine sales had negative correlations for each variable, namely, exchange rate, population growth, and cancer incidence. On the contrary, there was a positive correlation between non-small-cell lung cancer and relevant variables. Innovative medicine groups such as monoclonal antibodies and protein kinase inhibitor consumption showed a negative correlation. Conclusion: According to our results, pricing strategy may be an access barrier for oncology medicines in Türkiye. It should be reviewing the pricing policy that is beneficial for oncology medicine access in Türkiye.

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

BackgroundThe detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article.Results2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating.ConclusionThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Gaming expertise induces meso‑scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlying mechanisms related to plasticity-associated brain structural changes and their impact on brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso‑scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, to generate simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs’ simulated functional connectivity was characterized by a meso‑scale integration, with increased local connectivity in frontal, parietal, and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso‑scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light on the mechanisms underlying structural neural plasticity triggered by video game experiences.

Social hotspots in the automotive industry’s aluminum value chains—combining primary and generic data

Abstract Purpose The automotive industry and its suppliers have been facing increasing challenges in the sustainable sourcing of raw materials on environmental and social levels. To identify social risks in the value chain of aluminum in the automotive sector, this study aimed to examine how a generic hotspot analysis based on S-LCA can be conducted and support the reporting according to new legislative requirements such as the German Act on Corporate Due Diligence Obligations in Supply Chains. Methods This study investigated the social risks in the supply chains of two selected aluminum components of an aluminum company using a generic social hotspot analysis based on the UNEP Guidelines for Social Life Cycle Assessment of Products and Organizations 2020. The study employed the generic PSILCA© database and different additional indicators for the assessment. The UNEP Guidelines 2020 used in combination with the associated methodological sheets were beneficial in structuring the procedure within the social hotspot analysis. They gave the inexperienced user a guide, which led to the results presented in this work. Results and discussion A high number of social hotspots in upstream processes outside Europe across the different investigated indicators for both supply chains assessed were detected. Most social risks were identified in Guinea, as well as in a European and an Asian Country in the stakeholder categories “Workers” and “Local community.” Especially, the alloy element production in the Asian Country and the bauxite mining in Guinea showed an above-average number of social hotspots. The implementation of the work was associated with an enormous effort in information collection about the supply chains to be investigated and depended on the direct suppliers’ willingness to contribute. The need for a safe and supporting environment for suppliers to enhance transparency was identified. Conclusions Combining generic and company data to conduct a social hotspot assessment (e.g., using PSILCA©) to meet legislative requirements proved to be a suitable approach for complex supply chains such as the automotive one. Despite this, limitations of the PSILCA© database and potential for improvement were acknowledged regarding industry-wide social assessments of supply chains. Additionally, the study serves as an application example of the UNEP Guidelines 2020 and the activity variables for science in practice. Further development and standardization of social indicators and activity variables for impact measurement were identified as a research priority.

Mean curvature flow with generic initial data

We show that the mean curvature flow of generic closed surfaces in R3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathbb{R}^{3}$\\end{document} avoids asymptotically conical and non-spherical compact singularities. We also show that the mean curvature flow of generic closed low-entropy hypersurfaces in R4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathbb{R}^{4}$\\end{document} is smooth until it disappears in a round point. The main technical ingredient is a long-time existence and uniqueness result for ancient mean curvature flows that lie on one side of asymptotically conical or compact shrinking solitons.

A toolkit for planning and implementing acute febrile illness (AFI) surveillance.

Acute febrile illness (AFI) is a broad clinical syndrome with a wide range of potential infectious etiologies. The lack of accessible, standardized approaches to conducting AFI etiologic investigations has contributed to significant global gaps in data on the epidemiology of AFI. Based on lessons learned from years of supporting AFI sentinel surveillance worldwide, the U.S. Centers for Disease Control and Prevention developed the toolkit for planning and implementing AFI surveillance, described here. This toolkit provides a comprehensive yet flexible framework to guide researchers, public health officials, and other implementers in developing a strategy to identify and/or monitor the potential causes of AFI. The toolkit comprises a cohesive set of planning aids and supporting materials, including an implementation framework, generic protocol, several generic forms (including screening, case report, specimen collection and testing, and informed consent and assent), and a generic data dictionary. These materials incorporate key elements intended to harmonize approaches for AFI surveillance, as well as setting-specific components and considerations for adaptation based on local surveillance objectives and limitations. Appropriate adaptation and implementation of this toolkit may generate data that expand the global AFI knowledge base, strengthen countries' surveillance and laboratory capacity, and enhance outbreak detection and response efforts.

Long time stability for the derivative nonlinear Schrödinger equation

In this paper, we consider the long time dynamics of the solutions of the derivative nonlinear Schrödinger equation on one dimensional torus without external parameters. By using rational normal form, we prove the long time stability for generic small initial data.

Risk-Informing Nuclear Reactor Safety: The Prediction of the Probability of Core Damage Due to Loss of Power and Cooling

Abstract The modern idea of risk-informed decision-making (RIDM) is here critically examined for all existing, advanced and, generation-IV nuclear reactor systems. Motivated by the predictive difficulties of probabilistic risk assessment (PRA) in regard to occurred accidents, it is evident that the real (not hypothetical) consequences of nuclear core damage accidents that must be considered and quantified are the financial losses, infrastructure damages, societal disruptions, and adverse political policies, and not solely the traditional exceedance of regulated radiation release or public exposure limits. With this perspective, a new dynamic analysis is presented for estimating the probability of core damage due to extended loss of power and cooling in a modern nuclear reactor, giving results different from current standardized PRA/PSA analyses. Using existing data, we approach the multitude of different events in a new way: whatever the initial event in the finite event set {external flood, fire, hurricane, ice storm, typhoon, earthquake, cyber attack…}, the fundamental concerns are the consequent loss and nonrestoration of power, and the inadequate cooling of the core. The present proposed quantitative evaluation uses applicable and fully “exchangeable” severe event data for nuclear and nonnuclear systems, including active and passive emergency back-up systems for a wide range of power losses that lead to widespread damage and societal disruption. While not design-specific, this new independent “order-of-magnitude” estimate for the probability of core damage is some two to two hundred times larger than that shown or reported in recent modern and formal PSA/PRA for licensing submissions using generic failure rate data rather than actual severe event data. This new finding suggests greater uncertainties exist than presently assumed for risk-informed decision-making (RIDM), and points to the need for a major reconsideration and updating of risk assessment and regulatory risk-informed approaches for nuclear plant safety evaluation.

Climate change substitution factors for Canadian forest-based products and bioenergy

Evaluating the climate change mitigation potential of the forest sector requires a holistic approach based on forest carbon (C) sequestration, C storage in harvested wood products (HWP) and substitution on markets. High uncertainty is associated with substitution factors, that express avoided fossil greenhouse gas (GHG) emissions from the use of forest-based products in replacement of GHG-intensive materials and fossil fuels. Few studies have focused on the development of substitution factors in Canada, resulting in the use of unrepresentative generic data. Here, we provide a framework to reduce uncertainties related to substitution factors for primary wood products in a Canadian context. A life cycle assessment framework is used to quantify fossil GHG emissions for a baseline and a wood-intensive scenario. For solid product substitution, we focused on the construction sector and analyzed a range of innovative wood buildings with steel and reinforced concrete as alternative materials. We found non-weighted averages of 0.80 tC/tC for sawnwood and 0.81 tC/tC for panels. For energy substitution, we analyzed cases with different specifications on biomass product, facility type and alternative fossil fuel source in non-residential heat production and biofuel transportation sectors. We found a non-weighted average of 0.80 tC/tC for non-residential heat production and 0.51 tC/tC for biofuel transportation, that can be interpreted as 0.91 tC/tC for heavy fuel oil, 0.69 tC/tC for light fuel oil and 0.68 tC/tC for natural gas substitution. These results provide a benchmark for substitution factors in Canada, to help guide forest management strategies for climate change mitigation.

Conceptual design of a generic data harmonization process for OMOP common data model

BackgroundTo gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM.MethodsFor this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps.ResultsFrom 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps.ConclusionsThe generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.

On the hierarchy and fine structure of blowups and gradient catastrophes for multidimensional homogeneous Euler equation *

Blowups of derivatives and gradient catastrophes for the n-dimensional homogeneous Euler equation are discussed. It is shown that, in the case of generic initial data, the blowups exhibit a fine structure in accordance with the admissible ranks of certain matrix generated by the initial data. Blowups form a hierarchy composed by n + 1 levels with the singularity of derivatives given by ∂ui/∂xk∼|δx|−(m+1)/(m+2) , m=1,…,n along certain critical directions. It is demonstrated that in the multi-dimensional case there is certain bounded linear superposition of blowup derivatives. Particular results for the potential motion are presented too. Hodograph equations are basic tools of the analysis.

Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy.

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.

Can LCA be FAIR? Assessing the status quo and opportunities for FAIR data sharing

AimThe purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR (findability, accessibility, interoperability, and reuse) data principles.MethodsThis study investigates how is LCA data from publicly funded research currently shared. Firstly, the focus is on life cycle inventory data shared in journal articles. Given that FAIR data sharing is not only the responsibility of the LCA practitioner, this study further investigates guidelines (e.g., data sharing standards and data management plans) and infrastructure (repositories, data formats, and nomenclature) to identify the tools and services available to LCA community which are essential to enable FAIR data sharing.ResultsThe study identifies that although there is growing awareness to improve data sharing practices, implementation of FAIR guidelines for data sharing is seldom seen in practice. LCA studies that adhere to FAIR principles are primarily due to use of generic data repositories which provide tools to support data sharing. However, there is no guidance on how LCA specific data should be shared to ensure its findability, accessibility, interoperability, and reusability. This study suggests a workflow to enable FAIRification of LCA data. In addition, the study recommends further efforts within the LCA community on skill and technology development, strategic funding, and recognition of the best practices in relation to data sharing.ConclusionIn conclusion, this study highlights the necessity of data sharing incentives, guidelines, and platforms/repositories specific for the LCA community.

Generic Soft Error Data and Control Flow Error Detection by Instruction Duplication

Transient faults or soft errors are considered one of the most daunting reliability challenges for microprocessors. Software solutions for soft error protection are attractive because they can provide flexible and effective error protection. For instance, nZDC [1] state-of-the-art instruction duplication error protection scheme achieves a high degree of error detection by verifying the results of memory write operations and utilizes an effective control-flow checking mechanism. However, nZDC control-flow checking mechanism is architecture-dependent and suffers from some vulnerability holes. In this work, we address these issues by substituting nZDC control-flow checking mechanism with a general (ISA-independent) scheme and propose two transformations, coarse-grained scheduling, and asymmetric control-flow signatures, for hard-to-detect control flow errors. Fault injection experiments on different hardware components of synthesizable Verilog description of an OpenRISC-based microprocessor reveal that the proposed transformation shows 85% less silent data corruptions compared to nZDC. In addition, programs protected by the proposed scheme run on average around 37% faster than nZDC-protected programs.

Potentiality of automatic parameter tuning suite available in ACTS track reconstruction software framework

Particle tracking is among the most sophisticated and complex part of the full event reconstruction chain. A number of reconstruction algorithms work in a sequence to build these trajectories from detector hits. Each of these algorithms use many configuration parameters that need to be fine-tuned to properly account for the detector/experimental setup, the available CPU budget and the desired physics performance. Few examples of such parameters include the cut values limiting the search space of the algorithm, the approximations accounting for complex phenomena or the parameters controlling algorithm performance. The most popular method to tune these parameters is hand-tuning using brute-force techniques. These techniques can be inefficient and raise issues for the long-term maintainability of such algorithms. The opensource track reconstruction software framework known as “A Common Tracking Framework (ACTS)” offers an alternative solution to these parameter tuning techniques through the use of automatic parameter optimization algorithms. ACTS come equipped with an auto-tuning suite that provides necessary setup for performing optimization of input parameters belonging to track reconstruction algorithms. The user can choose the tunable parameters in a flexible way and define a cost/benefit function for optimizing the full reconstruction chain. The fast execution speed of ACTS allows the user to run several iterations of optimization within a reasonable time bracket. The performance of these optimizers has been demonstrated on different track reconstruction algorithms such as trajectory seed reconstruction and selection, particle vertex reconstruction and generation of simplified material map, and on different detector geometries such as Generic Detector and Open Data Detector (ODD). We aim to bring this approach to all aspects of trajectory reconstruction by having a more flexible integration of tunable parameters within ACTS.

Interactive low delay music and speech communication via network connections (OVBOX)

The “OVBOX” is a tool for low-delay network audio communication and generic data transmission between multiple clients. Acoustic end-to-end delays of about 30 ms can be achieved, assuming a good internet connection and a distance between clients of less than about 1500 km. For optimal spatial perception, e.g., when using multiple instruments of similar timbre, an individual 3-dimensional room acoustic simulation based on physical modelling is applied in each client. The system is optimised for headless operation using a dedicated single-board computer (Raspberry Pi 4B), but desktop clients are also available for better integration with other audio software. A client-server system allows remote configuration and automatic traversal of network address translation routers and firewalls. With the low latency that can be achieved, the “OVBOX” is used for music applications such as distributed rehearsals or concerts. Other applications include hearing research to achieve interactive speech communication with low delay transmission of head movements for real-time control of virtual reality, and transmission of other biophysical data for online analysis or central data logging. The tool is fully open source.

Collection and analysis of hydrocarbon gas buried onshore pipeline accidents in Indonesia as the databases for failure frequency assessment in a quantitative risk analysis

AbstractAs of May 31, 2022, approximately 18,687 km of gas pipeline has been built in Indonesia. However, the pipeline failure data are yet to be gathered or evaluated. In quantitative risk assessment (QRA), the pipeline failure frequency is calculated by a generic failure frequency approach. Most of these generic failure frequency data are based on other region pipeline incident databases. This paper aims to provide the basis of data for the failure frequency of Indonesia's buried onshore pipeline. Accident data from 1127.9 km of underground onshore gas pipelines within 30 years (1976–2006) in Indonesia were gathered and assessed to provide the cause and effect of pipeline age and diameter on the generic failure rate. This research reveals the average failure rate of 5.32E‐04/km‐year of onshore pipelines in Indonesia. The study concluded that baseline failure frequencies from this analysis could be used for buried onshore pipeline quantitative risk assessment purposes. Further pipeline failure research for a relatively current period (e.g., from 2006 to 2022), type of installation (e.g., buried, or non‐buried), and other applications of the pipeline (e.g., offshore pipeline) should be conducted as a way forward for this study to have a complete database for Indonesian accidents.

Decarbonisation Index (DCI): an LCA-based key performance indicator for the automotive industry

PurposeRoad vehicles cause considerable amounts of CO2e emissions over their life cycle. Original equipment manufacturers (OEMs) report CO2e emissions of past years on corporate level according to the Greenhouse Gas (GHG) Protocol and produce vehicle LCAs to identify GHG hotspots on the product-level. However, no approach to combine this past and future orientation of emissions modelling on fleet and product level yet exists. We demonstrate that this research gap is closed by using the “Decarbonisation Index” (DCI).MethodsWe identify the missing elements of OEMs’ absolute emissions reporting and vehicle LCAs to develop a resource-efficient, dynamic and modular key performance indicator (KPI) addressing both past and future scope 1–3 GHG emissions of OEMs. We assess in how far other existing approaches such as the Organisational LCA (O-LCA), the Organisation Environmental Footprint (OEF) and modular LCA can be used to develop these missing elements in a holistic modelling approach. After the derivation of the DCI, we provide a list of modelling options and data sources showing that the DCI can serve different means of an OEM, from a rough estimate of emissions to a basis for a detailed decarbonisation steering model involving several brands and departments.Results and discussionIn the case study, we compute a 2015 and 2035 DCI (in t CO2e per vehicle) by using the basic DCI calculation model and publicly available data of the Volkswagen Group as well as data derived from publicly available scenarios. We demonstrate that even with this simplistic approach, the DCI delivers meaningful results indicating the core measures for an OEM’s decarbonisation programme: an electrified fleet with renewable energy sources being used throughout the supply chain and use phase. A Monte Carlo simulation of the 2015 results demonstrate the DCI’s robustness regarding the identification of core measures but also its dependency on changing (external or internal) methodological requirements.ConclusionsThe DCI can be used by OEMs regardless of their company structure, powertrain portfolio or market coverage to monitor past emissions and model future emissions. The DCI combines the product-level of the vehicle LCA with the fleet-level necessary to develop a decarbonisation strategy. Its modular approach facilitates the use of generic LCA data or supplier-specific data on component level. Incrementally incorporating supplier-specific data is crucial to calculate the effect of real-world reduction measures in relation to generic databases used so far. An adaptation of the methodology to newly available data and regulations is thus possible and necessary. By adjusting past-reported DCI values to a new methodological set, an OEM’s decarbonisation progress can be analysed albeit the constantly developing methodology.

Open‐source photovoltaic model pipeline validation against well‐characterized system data

AbstractAll freely available plane‐of‐array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib‐python and pvpltools‐python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853‐1 and 61853‐2 testing, and the input data for each model were sourced from these system‐specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system‐specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system‐specific data.

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case

BackgroundIn clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. MethodsWe designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. ResultsMeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. ConclusionsThe MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.