Cargando…

From models to tools: clinical translation of machine learning studies in psychosis

Detalles Bibliográficos
Autores principales:	Mechelli, Andrea, Vieira, Sandra
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2020
Materias:	Comment
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7021680/ https://www.ncbi.nlm.nih.gov/pubmed/32060287 http://dx.doi.org/10.1038/s41537-020-0094-8

Ejemplares similares

Predictive tools in psychosis: what is ‘good enough’?
por: Seyedsalehi, Aida, et al.
Publicado: (2023)

Presenting machine learning model information to clinical end users with model facts labels
por: Sendak, Mark P., et al.
Publicado: (2020)

Machine learning for brain age prediction: Introduction to methods and clinical applications
por: Baecker, Lea, et al.
Publicado: (2021)

Machine learning COVID-19 detection from wearables
por: Nestor, Bret, et al.
Publicado: (2023)

Machine learning for chemical discovery
por: Tkatchenko, Alexandre
Publicado: (2020)

Clinical translation of silica nanoparticles
por: Janjua, Taskeen Iqbal, et al.
Publicado: (2021)

Neuroethics at the interface of machine learning and schizophrenia
por: McFarlane, Jacob, et al.
Publicado: (2020)

Mitigating bias in machine learning for medicine
por: Vokinger, Kerstin N., et al.
Publicado: (2021)

Mood by microbe: towards clinical translation
por: Dinan, Timothy G., et al.
Publicado: (2016)

Machine learning and earthquake forecasting—next steps
por: Beroza, Gregory C., et al.
Publicado: (2021)

Machine learning and health need better values
por: Ghassemi, Marzyeh, et al.
Publicado: (2022)

Translating genomic medicine to the clinic: challenges and opportunities
por: Zhang, Huan, et al.
Publicado: (2019)

Retrospective on a decade of machine learning for chemical discovery
por: von Lilienfeld, O. Anatole, et al.
Publicado: (2020)

Machine learning for COVID-19—asking the right questions
por: Bachtiger, Patrik, et al.
Publicado: (2020)

Machine learning for psychiatry: getting doctors at the black box?
por: Hedderich, Dennis M., et al.
Publicado: (2020)

Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence
por: Vieira, Sandra, et al.
Publicado: (2020)

Machine learning assessment of risk factors for depression in later adulthood
por: Zhang, Fengqing, et al.
Publicado: (2022)

Migraine genetics: from genome-wide association studies to translational insights
por: Gormley, Padhraig, et al.
Publicado: (2016)

S126. GOOD OUTCOME IN INDIVIDUALS AT ULTRA-HIGH RISK (UHR) OF DEVELOPING PSYCHOSIS: A DELPHI STUDY
por: Petros, Natalia, et al.
Publicado: (2018)

Introduction to the National Cancer Imaging Translational Accelerator (NCITA): a UK-wide infrastructure for multicentre clinical translation of cancer imaging biomarkers
por: McAteer, M. A., et al.
Publicado: (2021)

Machine learning-based global maps of ecological variables and the challenge of assessing them
por: Meyer, Hanna, et al.
Publicado: (2022)

Lost in translation
por: Petsko, Gregory A
Publicado: (2010)

The challenges and opportunities of scRNA-seq in COVID-19 research and clinical translation
por: Liu, Wendao, et al.
Publicado: (2022)

Translation of predictive modeling and AI into clinics: a question of trust
por: Caspers, Julian
Publicado: (2021)

Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation
por: Nixon, Kristen, et al.
Publicado: (2022)

Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders
por: Scarpazza, C., et al.
Publicado: (2020)

Translating the vaginal microbiome: gaps and challenges
por: Ravel, Jacques, et al.
Publicado: (2016)

Developing and translating knowledge for healthy ageing
por: Okayasu, Hiromasa, et al.
Publicado: (2023)

The ghost and the machine
por: Jobling, Mark A
Publicado: (2010)

Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach
por: Salmeri, Noemi, et al.
Publicado: (2023)

Early-phase clinical trials in a pandemic: learning from the response to COVID-19
por: Horsley, Alex, et al.
Publicado: (2022)

Knowledge translation in the reality of Brazilian public health
por: de Andrade, Keitty Regina Cordeiro, et al.
Publicado: (2020)

Translating transmissibility measures into recommendations for coronavirus prevention
por: Diaz-Quijano, Fredi Alexander, et al.
Publicado: (2020)

Cup-ling oskar RNA localization and translational control
por: Lasko, Paul
Publicado: (2003)

Patients as research partners in preference studies: learnings from IMI-PREFER
por: Smith, Meredith Y., et al.
Publicado: (2023)

Learning from a retraction
por: The Editors of the Lancet Group
Publicado: (2020)

Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk
por: Fusar-Poli, Paolo, et al.
Publicado: (2019)

Weakly-supervised deep learning models in computational pathology
por: Augustine, Tanya N.
Publicado: (2022)

The crucial need for tuberculosis translational research in the time of COVID-19
por: Loh, Fei Kean, et al.
Publicado: (2022)

Who’s learning? Using within-family studies to understand personalized learning
por: von Stumm, Sophie, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_ojgsdq4m4g760girjm3oksj61t