Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

Helgheim, Berit Irene; Maia, Rui Leandro; Ferreira, Joao C Ferreira or Joao; Martins, Ana

doi:10.3390/ijerph16050769

Cited by 16 publications

(5 citation statements)

References 39 publications

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“…Thus, efforts to optimize preclinical PTSD models for reliability/reproducibility is essential for promoting mechanistic understanding of the disease and enhance their ability to serve as effective platforms for evaluating new and promising therapeutics (Zhang et al, 2019). Efforts to achieve methodological consensus within a preclinical PTSD model can also facilitate meta-analyses and the creation of metadata, which serve as powerful strategies in translational research (Helgheim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Systematic Review and Methodological Considerations for the Use of Single Prolonged Stress and Fear Extinction Retention in Rodents

Ferland‐Beckham

Chaby

Daskalakis

et al. 2021

Front. Behav. Neurosci.

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Posttraumatic stress disorder (PTSD) is a mental health condition triggered by experiencing or witnessing a terrifying event that can lead to lifelong burden that increases mortality and adverse health outcomes. Yet, no new treatments have reached the market in two decades. Thus, screening potential interventions for PTSD is of high priority. Animal models often serve as a critical translational tool to bring new therapeutics from bench to bedside. However, the lack of concordance of some human clinical trial outcomes with preclinical animal efficacy findings has led to a questioning of the methods of how animal studies are conducted and translational validity established. Thus, we conducted a systematic review to determine methodological variability in studies that applied a prominent animal model of trauma-like stress, single prolonged stress (SPS). The SPS model has been utilized to evaluate a myriad of PTSD-relevant outcomes including extinction retention. Rodents exposed to SPS express an extinction retention deficit, a phenotype identified in humans with PTSD, in which fear memory is aberrantly retained after fear memory extinction. The current systematic review examines methodological variation across all phases of the SPS paradigm, as well as strategies for behavioral coding, data processing, statistical approach, and the depiction of data. Solutions for key challenges and sources of variation within these domains are discussed. In response to methodological variation in SPS studies, an expert panel was convened to generate methodological considerations to guide researchers in the application of SPS and the evaluation of extinction retention as a test for a PTSD-like phenotype. Many of these guidelines are applicable to all rodent paradigms developed to model trauma effects or learned fear processes relevant to PTSD, and not limited to SPS. Efforts toward optimizing preclinical model application are essential for enhancing the reproducibility and translational validity of preclinical findings, and should be conducted for all preclinical psychiatric research models.

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Section: Introductionmentioning

confidence: 99%

Systematic Review and Methodological Considerations for the Use of Single Prolonged Stress and Fear Extinction Retention in Rodents

Ferland‐Beckham

Chaby

Daskalakis

et al. 2021

Front. Behav. Neurosci.

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show abstract

“…The design of an ETL process faces several challenges, including the following: (1) The ETL process should be able to process huge amounts of data at once [62]; (2) the ETL process should be repeatable-if the source data change, the ETL process needs to be rerun to process the source data (Observational Health Data Sciences and Informatics [OHDSI]) [63]; (3) expert-level anonymization methodologies might be integrated into ETL workflows whenever possible [61]; and (4) there is a need to check for loss of data and compromised data integrity. The latter was highlighted in a recent study, in which inaccurate cohort identification took place because erroneous vocabulary mappings of a common data model were used (eg, ETL programming bugs and errors not captured during the quality assurance stages) [64].…”

Section: Insightmentioning

confidence: 99%

Ten Topics to Get Started in Medical Informatics Research

Wolfien,

Ahmadi,

Fitzer

et al. 2023

J Med Internet Res

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The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

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“…Healthcare data integration is a vital investigation topic involving patient privacy issues and dealing with different information systems. There is already some work under this topic [1] and [2]; nevertheless, isolated data from a single source is insufficient. Data must be enriched by adding further information (metadata) and integrating it with other data sources.…”

Section: Introductionmentioning

confidence: 99%

Information Sharing through Digital Service Agreement

Elvas

Ferreira

Helgheim

2023

Preprint

Self Cite

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Data sharing and services reuse in the health sector is a significant problem due to privacy, and security issues. The European Commission has classified health data as a unique resource owing to the ability to do both prospective and retrospective research at a low cost. Similarly, the OECD encourages member nations to create and implement health data governance systems that protect individual privacy while allowing data sharing. This paper aimed to describe a conceptual framework to allow medical information sharing among health entities in a secure environment. A framework of shared Artificial Intelligent services is proposed to provide a safe environment for information sharing based on digital services agreements (DSA) and a shared services infrastructure for artificial intelligence (AI) and knowledge creation: From the collaborative platform with privacy, health data can be shared, and shared analytics services will allow an easy and fast application of AI algorithms. The framework allows data prosumers (producers/consumers) to easily express their preferences on sharing their data, which analytics operations can be performed on such data, and by whom the resulting data can be shared, among other relevant aspects. This entails a framework that combines several technologies for expressing and enforcing data-sharing agreements and technologies to perform data analytics operations compliant. Among these technologies, we can mention data-centric policy enforcement mechanisms and data analysis operations directly performed on encrypted data provided by multiple prosumers. The framework is mainly based on an Information Sharing Infrastructure (ISI) and an Information Analysis Infrastructure (IAI) that can be deployed in several ways and on several devices (from cloud to mobile devices).

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Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

Cited by 16 publications

References 39 publications

Systematic Review and Methodological Considerations for the Use of Single Prolonged Stress and Fear Extinction Retention in Rodents

Systematic Review and Methodological Considerations for the Use of Single Prolonged Stress and Fear Extinction Retention in Rodents

Ten Topics to Get Started in Medical Informatics Research

Information Sharing through Digital Service Agreement

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