Dataset Information

Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers.

ABSTRACT: The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinical practices. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional "map" called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding ofthe field.

SUBMITTER: Kato A

PROVIDER: S-EPMC7746554 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers.

Kato Ayaka A Kunisato Yoshihiko Y Katahira Kentaro K Okimura Tsukasa T Yamashita Yuichi Y

Frontiers in psychiatry 20201204

The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinical practices. However, the multidisciplinary nature ...[more]

PMID: 33343418

Similar Datasets

Project description:Research in computer systems often involves the engineering, implementation, and measurement of complex systems software and data. The availability of these artifacts is critical to the reproducibility and replicability of the research results, because system software often embodies numerous implicit assumptions and parameters that are not fully documented in the research article itself. Artifact availability has also been previously associated with higher paper impact, as measured by citations counts. And yet, the sharing of research artifacts is still not as common as warranted by its importance. The primary goal of this study is to provide an exploratory statistical analysis of the artifact-sharing rates and associated factors in the research field of computer systems. To this end, we explore a cross-sectional dataset of papers from 56 contemporaneous systems conferences. In addition to extensive data on the conferences, papers, and authors, this analyze dataset includes data on the release, ongoing availability, badging, and locations of research artifacts. We combine this manually curated dataset with citation counts to evaluate the relationships between different artifact properties and citation metrics. Additionally, we revisit previous observations from other fields on the relationships between artifact properties and various other characteristics of papers, authors, and venue and apply them to this field. The overall rate of artifact sharing we find in this dataset is approximately 30%, although it varies significantly with paper, author, and conference factors, and it is closer to 43% for conferences that actively evaluated artifact sharing. Approximately 20% of all shared artifacts are no longer accessible four years after publications, predominately when hosted on personal and academic websites. Our main finding is that papers with shared artifacts averaged approximately 75% more citations than papers with none. Even after controlling for numerous confounding covariates, the release of an artifact appears to increase the citations of a systems paper by some 34%. This metric is further boosted by the open availability of the paper's text.

Project description:BackgroundAddressing recruitment and retention challenges in trials is a key priority for methods research, but navigating the literature is difficult and time-consuming. In 2016, ORRCA (www.orrca.org.uk) launched a free, searchable database of recruitment research that has been widely accessed and used to support the update of systematic reviews and the selection of recruitment strategies for clinical trials. ORRCA2 aims to create a similar database to map the growing volume and importance of retention research.MethodsSearches of Medline (Ovid), CINAHL, PsycINFO, Scopus, Web of Science Core Collection and the Cochrane Library, restricted to English language and publications up to the end of 2017. Hand searches of key systematic reviews were undertaken and randomised evaluations of recruitment interventions within the ORRCA database on 1 October 2020 were also reviewed for any secondary retention outcomes. Records were screened by title and abstract before obtaining the full text of potentially relevant articles. Studies reporting or evaluating strategies, methods and study designs to improve retention within healthcare research were eligible. Case reports describing retention challenges or successes and studies evaluating participant reported reasons for withdrawal or losses were also included. Studies assessing adherence to treatments, attendance at appointments outside of research and statistical analysis methods for missing data were excluded. Eligible articles were categorised into one of the following evidence types: randomised evaluations, non-randomised evaluations, application of retention strategies without evaluation and observations of factors affecting retention. Articles were also mapped against a retention domain framework. Additional data were extracted on research outcomes, methods and host study context.ResultsOf the 72,904 abstracts screened, 4,364 full texts were obtained, and 1,167 articles were eligible. Of these, 165 (14%) were randomised evaluations, 99 (8%) non-randomised evaluations, 319 (27%) strategies without evaluation and 584 (50%) observations of factors affecting retention. Eighty-four percent (n = 979) of studies assessed the numbers of participants retained, 27% (n = 317) assessed demographic differences between retained and lost participants, while only 4% (n = 44) assessed the cost of retention strategies. The most frequently reported domains within the 165 studies categorised as 'randomised evaluations of retention strategies' were participant monetary incentives (32%), participant reminders and prompts (30%), questionnaire design (30%) and data collection location and method (26%).ConclusionORRCA2 builds on the success of ORRCA extending the database to organise the growing volume of retention research. Less than 15% of articles were randomised evaluations of retention strategies. Mapping of the literature highlights several areas for future research such as the role of research sites, clinical staff and study design in enhancing retention. Future studies should also include cost-benefit analysis of retention strategies.

Project description:BackgroundThe significant proportion of patients suffering from subthreshold diagnoses such as partial posttraumatic stress disorder (PTSD) shows that today's diagnostic entities do not fully meet the reality and needs of clinical practice. Moreover, as stated also in the recently announced concept of research domain criteria (RDoC), the use of today's traditional diagnostic systems in psychiatric research does not sufficiently promote an integrative understanding of mental disorders across multiple units of analysis from behavior to neurobiology. Besides RDoC, core symptom-based research concepts have been proposed to bridge the translational gap in psychiatry, but, unfortunately, have not yet become the rule.Objective/methodFirst, this article briefly reviews literature on subthreshold PTSD (as an example for subthreshold diagnoses) and, second, pleas for and proposes a modified symptom-based research concept in psychiatry.ResultsSubthreshold PTSD has, like other subthreshold psychiatric diagnoses, not yet been clearly defined. Diagnostic entities such as subthreshold PTSD are subject to a certain arbitrariness as they are mainly the result of empiricism. This fact stresses the urgent need for neurobiologically-informed psychiatric diagnoses and motivated the here-presented proposal of a symptom-based research concept. As proposed here, and before by other researchers, symptom-based research in psychiatry should refrain from studying patient cohorts compiled according to diagnoses but, instead, should focus on assessing cohorts grouped according to chief complaints or predominant psychopathological symptoms.ConclusionsThe linkage of the RDoC concept and symptom-based psychiatric research might probably speed up the definition of biologically or symptom-based psychiatric diagnoses, which might replace the auxiliary constructs of "traditional" diagnoses such as full and subthreshold PTSD, and promote the development of novel psychological and pharmacological treatments.

Dataset Information

Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers.

Publications

Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets