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Strabismus Sep 2023Postural control is a complex skill based on the collaboration of dynamic sensory mechanisms, namely the visual, vestibular, and somatosensory systems. (Review)
Review
BACKGROUND
Postural control is a complex skill based on the collaboration of dynamic sensory mechanisms, namely the visual, vestibular, and somatosensory systems.
METHODS
A literature survey regarding postural stability in strabismus and amblyopia was conducted using databases in order to collect data for a narrative review of published reports and available literature.
RESULTS
The results of the literature survey were analyzed to provide an overview of the current knowledge of postural stability in strabismus and amblyopia. The results revealed that although postural control depends on the fundamental integration of three essential components (the visual, vestibular, and somatosensory systems), the role of vision is critical in postural stability. Once normal binocular vision is undesirably disrupted in childhood by some reason, especially in strabismus and/or amblyopia, balance is also affected. Abnormal balance affects coordination in gross and fine motor controls in school-age children and results in weakened academic performance and delayed social progress. It also impacts a child's general health, self-esteem, and safety.
CONCLUSIONS
Binocular vision is imperative for the maturation and preservation of balance control in children, as balance performance is reduced in strabismus and/or amblyopia.
Topics: Child; Humans; Amblyopia; Strabismus; Vision, Binocular; Data Management; Databases, Factual
PubMed: 37489251
DOI: 10.1080/09273972.2023.2236138 -
International Journal of Medical... Dec 2023In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing...
OBJECTIVES
In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer.
METHODS
We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept and tested it on colorectal cancer data, which is actual real-world data.
RESULTS
We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction.
CONCLUSIONS
In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system.
Topics: Humans; Data Accuracy; Reproducibility of Results; Data Management; Electronic Health Records; Colorectal Neoplasms
PubMed: 37871445
DOI: 10.1016/j.ijmedinf.2023.105262 -
Journal of Chemical Information and... Jul 2023A great advantage of computational research is its reproducibility and reusability. However, an enormous amount of computational research data in heterogeneous catalysis...
A great advantage of computational research is its reproducibility and reusability. However, an enormous amount of computational research data in heterogeneous catalysis is barricaded due to logistical limitations. Sufficient provenance and characterization of data and computational environment, with uniform organization and easy accessibility, can allow the development of software tools for integration across the multiscale modeling workflow. Here, we develop the Chemical Kinetics Database, CKineticsDB, a state-of-the-art datahub for multiscale modeling, designed to be compliant with the FAIR guiding principles for scientific data management. CKineticsDB utilizes a MongoDB back-end for extensibility and adaptation to varying data formats, with a referencing-based data model to reduce redundancy in storage. We have developed a Python software program for data processing operations and with built-in features to extract data for common applications. CKineticsDB evaluates the incoming data for quality and uniformity, retains curated information from simulations, enables accurate regeneration of publication results, optimizes storage, and allows the selective retrieval of files based on domain-relevant catalyst and simulation parameters. CKineticsDB provides data from multiple scales of theory (ab initio calculations, thermochemistry, and microkinetic models) to accelerate the development of new reaction pathways, kinetic analysis of reaction mechanisms, and catalysis discovery, along with several data-driven applications.
Topics: Data Management; Kinetics; Reproducibility of Results; Software
PubMed: 37436913
DOI: 10.1021/acs.jcim.3c00123 -
JAMIA Open Dec 2023Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of...
OBJECTIVES
Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud.
MATERIALS AND METHODS
Comprehensive Oncology Research Environment (CORE) Browser is a new open-source web application for cancer researchers to manage sequencing data organized in a flexible format in Amazon Simple Storage Service (S3) buckets. It has a microservices- and hypermedia-based architecture, which we integrated with Test-Driven Development (TDD), the iterative writing of computable specifications for how software should work prior to development. Relying on repeating patterns found in hypermedia-based architectures, we hypothesized that hypermedia would permit developing test "templates" that can be parameterized and executed for each microservice, maximizing code coverage while minimizing effort.
RESULTS
After one-and-a-half years of development, the CORE Browser backend had 121 test templates and 875 custom tests that were parameterized and executed 3031 times, providing 78% code coverage.
DISCUSSION
Architecting to permit test reuse through a hypermedia approach was a key success factor for our testing efforts. CORE Browser's application of hypermedia and TDD illustrates one way to integrate software engineering methods into data-intensive networked applications. Separating bioinformatic data management from analysis distinguishes this platform from others in bioinformatics and may provide stable data management while permitting analysis methods to advance more rapidly.
CONCLUSION
Software engineering practices are underutilized in informatics. Similar informatics projects will more likely succeed through application of good architecture and automated testing. Our approach is broadly applicable to data management tools involving cloud data storage.
PubMed: 37860604
DOI: 10.1093/jamiaopen/ooad089 -
JAMA Network Open Jul 2023
Topics: Humans; Data Management; International Classification of Diseases
PubMed: 37498604
DOI: 10.1001/jamanetworkopen.2023.27991 -
Journal of Pathology Informatics 2023The Pathology Informatics Bootcamp, held annually at the Pathology Informatics Summit, provides pathology trainees with essential knowledge in the rapidly evolving field... (Review)
Review
The Pathology Informatics Bootcamp, held annually at the Pathology Informatics Summit, provides pathology trainees with essential knowledge in the rapidly evolving field of Pathology Informatics. With a focus on data analytics, data science, and data management in 2022, the bootcamp addressed the growing importance of data analysis in pathology and laboratory medicine practice. The expansion of data-related subjects in Pathology Informatics Essentials for Residents (PIER) and the Clinical Informatics fellowship examinations highlights the increasing significance of these skills in pathology practice in particular and medicine in general. The curriculum included lectures on databases, programming, analytics, machine learning basics, and specialized topics like anatomic pathology data analysis and dashboarding.
PubMed: 37705688
DOI: 10.1016/j.jpi.2023.100331 -
Frontiers in Big Data 2024With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a...
With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning operations-centric data pipeline specifically designed for an energy consumption management system. This pipeline seamlessly integrates the machine learning model with real-time data management and prediction capabilities. The overall architecture of our proposed pipeline comprises several key components, including Kafka, InfluxDB, Telegraf, Zookeeper, and Grafana. To enable accurate energy consumption predictions, we adopt two time-series prediction models, long short-term memory (LSTM), and seasonal autoregressive integrated moving average (SARIMA). Our analysis reveals a clear trade-off between speed and accuracy, where SARIMA exhibits faster model learning time while LSTM outperforms SARIMA in prediction accuracy. To validate the effectiveness of our pipeline, we measure the overall processing time by optimizing the configuration of Telegraf, which directly impacts the load in the pipeline. The results are promising, as our pipeline achieves an average end-to-end processing time of only 0.39 s for handling 10,000 data records and an impressive 1.26 s when scaling up to 100,000 records. This indicates 30.69-90.88 times faster processing compared to the existing Python-based approach. Additionally, when the number of records increases by ten times, the increased overhead is reduced by 3.07 times. This verifies that the proposed pipeline exhibits an efficient and scalable structure suitable for real-time environments.
PubMed: 38562648
DOI: 10.3389/fdata.2024.1308236 -
Health Information Management : Journal... Jul 2023This article reports on a study that investigated data professionals in health care. The topic is interesting and relevant because of the ongoing trend towards...
BACKGROUND
This article reports on a study that investigated data professionals in health care. The topic is interesting and relevant because of the ongoing trend towards digitisation of the healthcare domain and efforts for it to become data driven, which entail a wide variety of work with data.
OBJECTIVE
Despite an interest in data science and more broadly in data work, we know surprisingly little about the people who work with data in healthcare. Therefore, we investigated data work at a large national healthcare data organisation in Denmark.
METHOD
An explorative mixed method approach combining a non-probability technique for design of an open survey with a target population of 300+ and 11 semi-structured interviews, was applied.
RESULTS
We report findings relevant to educational background, work identity, work tasks, and how staff acquired competences and knowledge, as well as what these attributes comprised. We found recurring themes of healthcare knowledge, data analytical skills, and information technology, reflected in education, competences and knowledge. However, there was considerable variation within and beyond those themes, and indeed most competences were learned "on the job" rather than as part of formal education.
CONCLUSION
a professional working with data in health care can be the result of different career paths. The most recurring work identity was that of "data analyst"; however, a wide variety of responses indicated that a stable data worker identity has not yet developed.
IMPLICATIONS
The findings present implications for educational policy makers and healthcare managers.
PubMed: 37491822
DOI: 10.1177/18333583231183083 -
Physiological Reviews Jul 2024Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable... (Review)
Review
Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.
Topics: Biomedical Research; Information Dissemination; Humans; Animals; Data Management
PubMed: 38451234
DOI: 10.1152/physrev.00043.2023 -
Journal of Sports Sciences Nov 2023Identifying tools and processes to effectively and efficiently evaluate technologies is an area of need for many sport stakeholders. This study aimed to develop a... (Review)
Review
Identifying tools and processes to effectively and efficiently evaluate technologies is an area of need for many sport stakeholders. This study aimed to develop a standardised, evidence-based framework to guide the evaluation of sports technologies. In developing the framework, a review of standards, guidelines and research into sports technology was conducted. Following this, 55 experts across the sports industry were presented with a draft framework for feedback. Following a two-round Delphi survey, the final framework consisted of 25 measurable features grouped under five quality pillars. These were 1) Quality Assurance & Measurement (), 2) Established Benefit , 3) Ethics & Security , 4) User Experience & 5) Data Management . The framework can be used to help design and refine sports technology in order to optimise quality and maintain industry standards, as well as guide purchasing decisions by organisations. It may also serve to create a common language for organisations, manufacturers, investors, and consumers to improve the efficiency of their decision-making relating to sports technology.
Topics: Humans; Reproducibility of Results; Sports; Technology; Forecasting
PubMed: 38305379
DOI: 10.1080/02640414.2024.2308435