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Clinical and Translational Science Aug 2023The purpose of this article is to propose and provide a blueprint for a graduate-level curriculum in clinical data science, devoted to the measurement, acquisition,...
The purpose of this article is to propose and provide a blueprint for a graduate-level curriculum in clinical data science, devoted to the measurement, acquisition, care, treatment, and inferencing of clinical research data. The curriculum presented here contains a series of five required core courses, five required research courses, and a list of potential electives. The coursework draws from but does not duplicate content from the foundational areas of biostatistics, clinical medicine, biomedical informatics, and regulatory affairs, and may be reproduced by any institution interested in and capable of offering such a program. This new curriculum in "clinical" data science will prepare students for work in academic, industry, and government research settings as well as offer a unifying knowledge base for the profession.
Topics: Humans; Data Management; Data Science; Models, Educational; Biometry; Curriculum
PubMed: 37587756
DOI: 10.1111/cts.13545 -
Applied Clinical Informatics Mar 2024Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience,...
BACKGROUND
Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training.
OBJECTIVES
The focus is the development and presentation of a data management approach specifically tailored for clinical researchers involved in active patient care, especially in the neuroscientific environment of German university hospitals. Our design considers the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the secure handling of sensitive data in compliance with the General Data Protection Regulation.
METHODS
We introduce a streamlined database concept, featuring an intuitive graphical interface built on Hypertext Markup Language revision 5 (HTML5)/Cascading Style Sheets (CSS) technology. The system can be effortlessly deployed within local networks, that is, in Microsoft Windows 10 environments. Our design incorporates FAIR principles for effective data management. Moreover, we have streamlined data interchange through established standards like HL7 Clinical Document Architecture (CDA). To ensure data integrity, we have integrated real-time validation mechanisms that cover data type, plausibility, and Clinical Quality Language logic during data import and entry.
RESULTS
We have developed and evaluated our concept with clinicians using a sample dataset of subjects who visited our memory clinic over a 3-year period and collected several multimodal clinical parameters. A notable advantage is the unified data matrix, which simplifies data aggregation, anonymization, and export. THIS STREAMLINES DATA EXCHANGE AND ENHANCES DATABASE INTEGRATION WITH PLATFORMS LIKE KONSTANZ INFORMATION MINER (KNIME): .
CONCLUSION
Our approach offers a significant advancement for capturing and managing clinical research data, specifically tailored for small-scale initiatives operating within limited information technology (IT) infrastructures. It is designed for immediate, hassle-free deployment by clinicians and researchers.The database template and precompiled versions of the user interface are available at: https://github.com/stebro01/research_database_sqlite_i2b2.git.
Topics: Humans; Data Management; Programming Languages
PubMed: 38301729
DOI: 10.1055/a-2259-0008 -
Trends in Cardiovascular Medicine Dec 2023Despite treatment advancements, HF mortality remains high, prompting interest in reducing HF-related hospitalizations through remote monitoring. These advances are... (Review)
Review
Despite treatment advancements, HF mortality remains high, prompting interest in reducing HF-related hospitalizations through remote monitoring. These advances are necessary considering the rapidly rising prevalence and incidence of HF worldwide, presenting a burden on hospital resources. While traditional approaches have failed in predicting impending HF-related hospitalizations, remote hemodynamic monitoring can detect changes in intracardiac filling pressure weeks prior to HF-related hospitalizations which makes timely pharmacological interventions possible. To ensure successful implementation, structural integration, optimal patient selection, and efficient data management are essential. This review aims to provide an overview of the rationale, the available devices, current evidence, and the implementation of remote hemodynamic monitoring.
PubMed: 38109949
DOI: 10.1016/j.tcm.2023.12.003 -
Journal of Registry Management 2023The past several years have been marked by substantial growth in pediatric cancer data and collection across the world. In the United States, multiple projects and...
The past several years have been marked by substantial growth in pediatric cancer data and collection across the world. In the United States, multiple projects and standard setters have laid a foundation for the growth of this data, and the need for an overview and explanation of a few of the programs directly relevant to cancer registrars has become apparent. This article will discuss 3 initiatives that highlight many of the efforts and intricacies involved with the collection of pediatric cancer data in the cancer registry world: the National Childhood Cancer Registry, the Toronto Pediatric Cancer Stage Guidelines, and the Pediatric Site-Specific Data Items Work Group.
Topics: Child; Humans; United States; Neoplasms; Registries; Neoplasm Staging; Data Management; Data Collection
PubMed: 37941745
DOI: No ID Found -
Drug Discovery Today Jun 2024Recent Allotrope Foundation (AF) Connect Workshops (2021-2023) showcased some of the latest advancements in data standardization for analytical data in the... (Review)
Review
Recent Allotrope Foundation (AF) Connect Workshops (2021-2023) showcased some of the latest advancements in data standardization for analytical data in the pharmaceutical industry. These workshops demonstrated the adaption of two key technologies, the Allotrope Data Format (ADF) and the Allotrope Simple Model (ASM), which streamline instrument data representation and terminology to enhance interoperability across systems. Notably, ASM has facilitated broader adoption of the standard. The increasing significance of data-driven decision-making in the life sciences is underscored by the evolving landscape of open-source solutions and commercial implementations, as demonstrated by industry leaders adopting these standards. Here, we highlight selected examples that illustrate the collective efforts of the community in advancing data standards and data management in the life sciences.
Topics: Humans; Drug Industry; Biological Science Disciplines
PubMed: 38642701
DOI: 10.1016/j.drudis.2024.103988 -
Current Opinion in Insect Science Dec 2023Understanding the rules of how monarch butterflies complete their annual North American migration will be clarified by studying them within a movement ecology framework.... (Review)
Review
Understanding the rules of how monarch butterflies complete their annual North American migration will be clarified by studying them within a movement ecology framework. Insect movement ecology is growing at a rapid pace due to the development of novel monitoring systems that allow ever-smaller animals to be tracked at higher spatiotemporal resolution for longer periods of time. New innovations in tracking hardware and associated software, including miniaturization, energy autonomy, data management, and wireless communication, are reducing the size and increasing the capability of next-generation tracking technologies, bringing the goal of tracking monarchs over their entire migration closer within reach. These tools are beginning to be leveraged to provide insight into different aspects of monarch biology and ecology, and to contribute to a growing capacity to understand insect movement ecology more broadly and its impact on human life.
Topics: Humans; Animals; Butterflies; Animal Migration; Ecology; Seasons
PubMed: 37678709
DOI: 10.1016/j.cois.2023.101111 -
Allergology International : Official... Apr 2024In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse...
BACKGROUND
In 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.
METHODS
We 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.
RESULTS
MeDIA 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.
CONCLUSIONS
The 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.
Topics: Humans; Dermatitis, Atopic; Data Management; Biomedical Research; Precision Medicine
PubMed: 38102028
DOI: 10.1016/j.alit.2023.11.006 -
Journal of the American Medical... Sep 2023Researchers at New York University (NYU) Grossman School of Medicine contacted the Health Sciences Library for help with locating large datasets for reuse. In response,...
OBJECTIVE
Researchers at New York University (NYU) Grossman School of Medicine contacted the Health Sciences Library for help with locating large datasets for reuse. In response, the library developed and maintained the NYU Data Catalog, a public-facing data catalog that has supported not only faculty acquisition of data but also the dissemination of the products of their research in various ways.
MATERIALS AND METHODS
The current NYU Data Catalog is built upon the Symfony framework with a tailored metadata schema reflecting the scope of faculty research areas. The project team curates new resources, including datasets and supporting software code, and conducts quarterly and annual evaluations to assess user interactions with the NYU Data Catalog and opportunities for growth.
RESULTS
Since its launch in 2015, the NYU Data Catalog underwent a number of changes prompted by an increase in the disciplines represented by faculty contributors. The catalog has also utilized faculty feedback to enhance support of data reuse and researcher collaboration through alterations to its schema, layout, and visibility of records.
DISCUSSION
These findings demonstrate the flexibility of data catalogs as a platform for enabling the discovery of disparate sources of data. While not a repository, the NYU Data Catalog is well-positioned to support mandates for data sharing from study sponsors and publishers.
CONCLUSION
The NYU Data Catalog makes the most of the data that researchers share and can be harnessed as a modular and adaptable platform to promote data sharing as a cultural practice.
Topics: Humans; New York; Universities; Software; Medicine
PubMed: 37414539
DOI: 10.1093/jamia/ocad125 -
BioRxiv : the Preprint Server For... Nov 2023Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that...
Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and data shifts all compromise model robustness and generalizability. Without proper tuning and data management, deploying machine learning models in the presence of unaccounted for corruptions leads to reduced or misleading performance. This study explores techniques to enhance model generalizability through iterative adjustments. Specifically, we investigate a detection tasks using electron microscopy images and compare models trained with different normalization and augmentation techniques. We found that models trained with Group Normalization or texture data augmentation outperform other normalization techniques and classical data augmentation, enabling them to learn more generalized features. These improvements persist even when models are trained and tested on disjoint datasets acquired through diverse data acquisition protocols. Results hold true for transformerand convolution-based detection architectures. The experiments show an impressive 29% boost in average precision, indicating significant enhancements in the model's generalizibality. This underscores the models' capacity to effectively adapt to diverse datasets and demonstrates their increased resilience in real-world applications.
PubMed: 38076794
DOI: 10.1101/2023.11.27.568889 -
Journal of Chemical Information and... Oct 2023The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle...
The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle (indable, ccessible, nteroperable, and eusable). For that, the annotation of research data with metadata is central. However, existing research data management workflows often require that metadata be created by the corresponding researchers, which takes effort and time. Here, we developed LISTER as a methodological and algorithmic solution to create and extract metadata from annotated, template-based experimental documentation using minimum effort. We focused on tailoring the integration between existing platforms by using eLabFTW as the electronic lab notebook and adopting the ISA (nvestigation, tudy, ssay) model as the abstract data model framework. LISTER consists of four components: annotation language to support metadata extraction; customized eLabFTW entries using specific hierarchies, templates, and tags to structure reusable scientific documentation; a "container" concept in eLabFTW, making metadata of a particular container content extractable along with its underlying, related experiments via a single click; a Python-based app to enable easy-to-use, semiautomated metadata extraction from eLabFTW entries. LISTER outputs metadata in machine-readable .json and human-readable .xlsx formats, and Material and Methods (MM) descriptions in .docx format that could be used in a thesis or manuscript. The metadata can be used as a basis to create or extend ontologies, which, when applied to the published research data, will significantly enhance its value. DSpace is used as a data cataloging platform for hosting the extracted metadata and research data. We applied LISTER to computational biophysical chemistry, protein biochemistry, and molecular biology, and our concept should be extendable to other life science areas.
PubMed: 37773594
DOI: 10.1021/acs.jcim.3c00744