Jose Arturo Santisteban, David Rotenberg, Stefan Kloiber, Marta M Maslej, Adeel Ansari, Bahar Amani, Darren Courtney, Farhat Farrokhi, Natalie Freeman, Masooma Hassan, Lucia Kwan, Mindaugas Mozuraitis, Michael Lau, Natalia Potapova, Farhad Qureshi, Nicole Schoer, Nelson Shen, Joanna Yu, Noelle Coombe, Kimberly Hunter, Peter Selby, Nicole Thomson, Damian Jankowicz, Sean L Hill
{"title":"大脑健康数据库：数据驱动的精神卫生保健和研究的系统方法。","authors":"Jose Arturo Santisteban, David Rotenberg, Stefan Kloiber, Marta M Maslej, Adeel Ansari, Bahar Amani, Darren Courtney, Farhat Farrokhi, Natalie Freeman, Masooma Hassan, Lucia Kwan, Mindaugas Mozuraitis, Michael Lau, Natalia Potapova, Farhad Qureshi, Nicole Schoer, Nelson Shen, Joanna Yu, Noelle Coombe, Kimberly Hunter, Peter Selby, Nicole Thomson, Damian Jankowicz, Sean L Hill","doi":"10.3389/fninf.2025.1616981","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Mental health care is undermined by fragmented data collection, as incomplete datasets can compromise treatment efficacy and research. The BrainHealth Databank (BHDB) at the Centre for Addiction and Mental Health (CAMH) establishes the governance and infrastructure for a Learning Mental Health System that integrates digital tools, measurement-based care, artificial intelligence (AI), and open science to deliver personalized, data-driven care.</p><p><strong>Methods: </strong>Central to the BHDB's approach is its comprehensive governance framework, which actively engages clinicians, researchers, data scientists, privacy and ethics experts, and patient and family partners. This codesigned approach ensures that digital health technologies are deployed ethically, securely, and effectively within clinical settings.</p><p><strong>Results: </strong>By aligning data collection with clinical and research goals and harmonizing over 12 million data points from 33,000 patient trajectories, the BHDB enhances data quality, enables real-time decision support, and fosters continuous improvement.</p><p><strong>Discussion: </strong>The BHDB provides a model for integrating AI and digital tools into mental health care, as well as research data collection, analyses, storage, and sharing through the BHDB Portal (https://bhdb.camh.ca).</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1616981"},"PeriodicalIF":2.5000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392782/pdf/","citationCount":"0","resultStr":"{\"title\":\"The BrainHealth Databank: a systems approach to data-driven mental health care and research.\",\"authors\":\"Jose Arturo Santisteban, David Rotenberg, Stefan Kloiber, Marta M Maslej, Adeel Ansari, Bahar Amani, Darren Courtney, Farhat Farrokhi, Natalie Freeman, Masooma Hassan, Lucia Kwan, Mindaugas Mozuraitis, Michael Lau, Natalia Potapova, Farhad Qureshi, Nicole Schoer, Nelson Shen, Joanna Yu, Noelle Coombe, Kimberly Hunter, Peter Selby, Nicole Thomson, Damian Jankowicz, Sean L Hill\",\"doi\":\"10.3389/fninf.2025.1616981\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Mental health care is undermined by fragmented data collection, as incomplete datasets can compromise treatment efficacy and research. The BrainHealth Databank (BHDB) at the Centre for Addiction and Mental Health (CAMH) establishes the governance and infrastructure for a Learning Mental Health System that integrates digital tools, measurement-based care, artificial intelligence (AI), and open science to deliver personalized, data-driven care.</p><p><strong>Methods: </strong>Central to the BHDB's approach is its comprehensive governance framework, which actively engages clinicians, researchers, data scientists, privacy and ethics experts, and patient and family partners. This codesigned approach ensures that digital health technologies are deployed ethically, securely, and effectively within clinical settings.</p><p><strong>Results: </strong>By aligning data collection with clinical and research goals and harmonizing over 12 million data points from 33,000 patient trajectories, the BHDB enhances data quality, enables real-time decision support, and fosters continuous improvement.</p><p><strong>Discussion: </strong>The BHDB provides a model for integrating AI and digital tools into mental health care, as well as research data collection, analyses, storage, and sharing through the BHDB Portal (https://bhdb.camh.ca).</p>\",\"PeriodicalId\":12462,\"journal\":{\"name\":\"Frontiers in Neuroinformatics\",\"volume\":\"19 \",\"pages\":\"1616981\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392782/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neuroinformatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fninf.2025.1616981\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fninf.2025.1616981","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
The BrainHealth Databank: a systems approach to data-driven mental health care and research.
Introduction: Mental health care is undermined by fragmented data collection, as incomplete datasets can compromise treatment efficacy and research. The BrainHealth Databank (BHDB) at the Centre for Addiction and Mental Health (CAMH) establishes the governance and infrastructure for a Learning Mental Health System that integrates digital tools, measurement-based care, artificial intelligence (AI), and open science to deliver personalized, data-driven care.
Methods: Central to the BHDB's approach is its comprehensive governance framework, which actively engages clinicians, researchers, data scientists, privacy and ethics experts, and patient and family partners. This codesigned approach ensures that digital health technologies are deployed ethically, securely, and effectively within clinical settings.
Results: By aligning data collection with clinical and research goals and harmonizing over 12 million data points from 33,000 patient trajectories, the BHDB enhances data quality, enables real-time decision support, and fosters continuous improvement.
Discussion: The BHDB provides a model for integrating AI and digital tools into mental health care, as well as research data collection, analyses, storage, and sharing through the BHDB Portal (https://bhdb.camh.ca).
期刊介绍：
Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states.
Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
