ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets
Abstract Background Nowadays, both researchers and clinicians alike have to deal with increasingly larger datasets, specifically also in the context of mental health data. Sophisticated tools for dataset visualization of information from various item-based instruments, such as questionnaire data or...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | European Psychiatry |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S0924933825024575/type/journal_article |
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| author | Karina Janson Karl Gottfried Olaf Reis Johannes Kornhuber Anna Eichler Michael Deuschle Tobias Banaschewski Frauke Nees IMAC-Mind Consortium |
| author_facet | Karina Janson Karl Gottfried Olaf Reis Johannes Kornhuber Anna Eichler Michael Deuschle Tobias Banaschewski Frauke Nees IMAC-Mind Consortium |
| author_sort | Karina Janson |
| collection | DOAJ |
| description | Abstract
Background
Nowadays, both researchers and clinicians alike have to deal with increasingly larger datasets, specifically also in the context of mental health data. Sophisticated tools for dataset visualization of information from various item-based instruments, such as questionnaire data or data from digital applications or clinical documentations, are still lacking, specifically for an integration at multiple levels and for use in both data organization and appropriate construction for its valid use in subsequent analyses.
Methods
Here, we introduce ItemComplex, a Python-based framework for ex-post visualization of large datasets. The method exploits the comprehensive recognition of instrument alignments and the identification of new content networks and graphs based on item similarities and shared versus differential conceptual bases within and across data and studies.
Results
The ItemComplex framework was evaluated using four existing large datasets from four different cohort studies and demonstrated successful data visualization across multi-item instruments within and across studies. ItemComplex enables researchers and clinicians to navigate through big datasets reliably, informatively, and quickly. Moreover, it facilitates the extraction of new insights into construct representations and concept identifications within the data.
Conclusions
The ItemComplex app is an efficient tool in the field of big data management and analysis addressing the growing complexity of modern datasets to harness the potential hidden within these extensive collections of information. It is also easily adjustable for individual datasets and user preferences, both in the research and clinical field.
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| format | Article |
| id | doaj-art-c947370b7ce04e7c9cbfbf8e7dc2c0e6 |
| institution | DOAJ |
| issn | 0924-9338 1778-3585 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Psychiatry |
| spelling | doaj-art-c947370b7ce04e7c9cbfbf8e7dc2c0e62025-08-20T02:40:00ZengCambridge University PressEuropean Psychiatry0924-93381778-35852025-01-016810.1192/j.eurpsy.2025.2457ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasetsKarina Janson0https://orcid.org/0000-0002-0902-0628Karl Gottfried1Olaf Reis2Johannes Kornhuber3Anna Eichler4Michael Deuschle5Tobias Banaschewski6https://orcid.org/0000-0003-4595-1144Frauke Nees7https://orcid.org/0000-0002-7796-8234IMAC-Mind ConsortiumDepartment of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany Institute of Medical Psychology and Medical Sociology, https://ror.org/01tvm6f46 University Medical Center Schleswig-Holstein, Kiel University, Kiel, GermanyDepartment of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, GermanyDepartment of Child and Adolescent Psychiatry, Neurology, Psychosomatics and Psychotherapy, Rostock University Medical Centre, Gehlsheimer Strasse 20, Rostock 18147, GermanyDepartment of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen 91054, GermanyDepartment of Child and Adolescent Mental Health, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen 91054, GermanyDepartment of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Mannheim, GermanyDepartment of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, GermanyDepartment of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany Institute of Medical Psychology and Medical Sociology, https://ror.org/01tvm6f46 University Medical Center Schleswig-Holstein, Kiel University, Kiel, GermanyAbstract Background Nowadays, both researchers and clinicians alike have to deal with increasingly larger datasets, specifically also in the context of mental health data. Sophisticated tools for dataset visualization of information from various item-based instruments, such as questionnaire data or data from digital applications or clinical documentations, are still lacking, specifically for an integration at multiple levels and for use in both data organization and appropriate construction for its valid use in subsequent analyses. Methods Here, we introduce ItemComplex, a Python-based framework for ex-post visualization of large datasets. The method exploits the comprehensive recognition of instrument alignments and the identification of new content networks and graphs based on item similarities and shared versus differential conceptual bases within and across data and studies. Results The ItemComplex framework was evaluated using four existing large datasets from four different cohort studies and demonstrated successful data visualization across multi-item instruments within and across studies. ItemComplex enables researchers and clinicians to navigate through big datasets reliably, informatively, and quickly. Moreover, it facilitates the extraction of new insights into construct representations and concept identifications within the data. Conclusions The ItemComplex app is an efficient tool in the field of big data management and analysis addressing the growing complexity of modern datasets to harness the potential hidden within these extensive collections of information. It is also easily adjustable for individual datasets and user preferences, both in the research and clinical field. https://www.cambridge.org/core/product/identifier/S0924933825024575/type/journal_articlebig dataconstructscontent networksdata navigationdata structuringdata visualizationitems |
| spellingShingle | Karina Janson Karl Gottfried Olaf Reis Johannes Kornhuber Anna Eichler Michael Deuschle Tobias Banaschewski Frauke Nees IMAC-Mind Consortium ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets European Psychiatry big data constructs content networks data navigation data structuring data visualization items |
| title | ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets |
| title_full | ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets |
| title_fullStr | ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets |
| title_full_unstemmed | ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets |
| title_short | ItemComplex: A Python-based visualization framework for ex-post organization and integration of large language-based datasets |
| title_sort | itemcomplex a python based visualization framework for ex post organization and integration of large language based datasets |
| topic | big data constructs content networks data navigation data structuring data visualization items |
| url | https://www.cambridge.org/core/product/identifier/S0924933825024575/type/journal_article |
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