Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment

Autor: Erik Perfalk, Martin Bernstorff, Andreas Aalkjær Danielsen, Søren Dinesen Østergaard

Publikováno v: European Psychiatry, Vol 67 (2024)

Abstract Background Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore

Externí odkaz: https://doaj.org/article/fd72fd52a379441f8ab3be44336aaf73

Validation of ratings on the six-item Positive and Negative Syndrome Scale obtained via the Simplified Negative and Positive Symptoms Interview among outpatients with schizophrenia

Autor: Cecilie Marie Nielsen, Pernille Kølbæk, David Dines, Martin Locht Pedersen, Andreas Aalkjær Danielsen, Camilla Holmgård, Sanne Wissing, Anne-Mette Esbøl, Nina Friis Bak Fuglsang, Tuan Dang Nguyen, Ole Mors, Mark Opler, Christoph U Correll, Søren Dinesen Østergaard

Publikováno v: Nielsen, C M, Kølbæk, P, Dines, D, Pedersen, M L, Danielsen, A A, Holmgård, C, Wissing, S, Esbøl, A-M, Fuglsang, N F B, Nguyen, T D, Mors, O, Opler, M, Correll, C U & Østergaard, S D 2022, ' Validation of ratings on the six-item Positive and Negative Syndrome Scale obtained via the Simplified Negative and Positive Symptoms Interview among outpatients with schizophrenia ', Journal of Psychopharmacology, vol. 36, no. 11, pp. 1208-1217 . https://doi.org/10.1177/02698811221131992

Background: The six-item Positive and Negative Syndrome Scale (PANSS-6) is a measure of the severity of core symptoms of schizophrenia, which can be administered via the brief Simplified Negative and Positive Symptoms Interview (SNAPSI). A recent stu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9b4cf7b029218d765b4a9679fe163ca
https://pubmed.ncbi.nlm.nih.gov/36268705

Monitoring of COVID-19 pandemic-related psychopathology using machine learning

Autor: Kenneth C. Enevoldsen, Andreas A. Danielsen, Christopher Rohde, Oskar H. Jefsen, Kristoffer L. Nielbo, Søren D. Østergaard

Publikováno v: Enevoldsen, K, Danielsen, A A, Rohde, C, Jefsen, O H, Nielbo, K L & Østergaard, S D 2022, ' Monitoring of COVID-19 pandemic-related psychopathology using machine learning ', Acta Neuropsychiatrica, vol. 34, no. 3, pp. 148-152 . https://doi.org/10.1017/neu.2022.2
Aarhus University
Enevoldsen, K C, Danielsen, A A, Rohde, C, Jefsen, O H, Nielbo, K L & Østergaard, S D 2022, ' Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning ', Acta Neuropsychiatrica, pp. 1-14 . https://doi.org/10.1017/neu.2022.2

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7497a9712212a09584f515ee7a222a7d
https://pure.au.dk/portal/da/publications/monitoring-of-covid19-pandemicrelated-psychopathology-using-machine-learning(833d8b52-c4c8-4e8c-9e1d-b2214daa9e5a).html

Effect of the GLP-1 receptor agonist semaglutide on metabolic disturbances in clozapine-treated or olanzapine-treated patients with a schizophrenia spectrum disorder: study protocol of a placebo-controlled, randomised clinical trial (SemaPsychiatry)

Autor: Marie Reeberg Sass, Andreas Aalkjær Danielsen, Ole Köhler-Forsberg, Heidi Storgaard, Filip K Knop, Mette Ødegaard Nielsen, Anders Mikael Sjödin, Ole Mors, Christoph U Correll, Claus Ekstrøm, Maj Vinberg, Jimmi Nielsen, Tina Vilsbøll, Anders Fink-Jensen

Publikováno v: Sass, M R, Danielsen, A A, Köhler-Forsberg, O, Storgaard, H, Knop, F K, Nielsen, M Ø, Sjödin, A M, Mors, O, Correll, C U, Ekstrøm, C, Vinberg, M, Nielsen, J, Vilsbøll, T & Fink-Jensen, A 2023, ' Effect of the GLP-1 receptor agonist semaglutide on metabolic disturbances in clozapine-treated or olanzapine-treated patients with a schizophrenia spectrum disorder : study protocol of a placebo-controlled, randomised clinical trial (SemaPsychiatry) ', BMJ Open, vol. 13, no. 1, e068652 . https://doi.org/10.1136/bmjopen-2022-068652
Sass, M R, Danielsen, A A, Köhler-Forsberg, O, Storgaard, H, Knop, F K, Nielsen, M Ø, Sjödin, A M, Mors, O, Correll, C U, Ekstrøm, C, Vinberg, M, Nielsen, J, Vilsbøll, T & Fink-Jensen, A 2023, ' Effect of the GLP-1 receptor agonist semaglutide on metabolic disturbances in clozapine-treated or olanzapine-treated patients with a schizophrenia spectrum disorder : study protocol of a placebo-controlled, randomised clinical trial (SemaPsychiatry) ', BMJ Open, vol. 13, no. 1, pp. e068652 . https://doi.org/10.1136/bmjopen-2022-068652
Sass, M R, Danielsen, A A, Köhler-Forsberg, O, Storgaard, H, Knop, F K, Nielsen, M Ø, Sjödin, A M, Mors, O, Correll, C U, Ekstrøm, C, Vinberg, M, Nielsen, J, Vilsbøll, T & Fink-Jensen, A 2023, ' Effect of the GLP-1 receptor agonist semaglutide on metabolic disturbances in clozapine-treated or olanzapine-treated patients with a schizophrenia spectrum disorder: study protocol of a placebo-controlled, randomised clinical trial (SemaPsychiatry) ', BMJ Open, vol. 13, no. 1, e068652 . https://doi.org/10.1136/bmjopen-2022-068652

IntroductionClozapine and olanzapine are some of the most effective antipsychotics, but both are associated with weight gain and relevant metabolic disturbances, including pre-diabetes and diabetes. Non-pharmacological/behavioural interventions have

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fef96437e68655bf583d30679468a16
https://doi.org/10.1136/bmjopen-2022-068652

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data

Autor: Kristoffer Laigaard Nielbo, Morten Fenger, Søren Dinesen Østergaard, Ole Mors, Andreas A. Danielsen

Publikováno v: Danielsen, A A, Fenger, M H J, Østergaard, S D, Nielbo, K L & Mors, O 2019, ' Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data ', Acta Psychiatrica Scandinavica, vol. 140, no. 2, pp. 147-157 . https://doi.org/10.1111/acps.13061
Danielsen, A A, Fenger, M H J, Østergaard, S D, Nielbo, K L & Mors, O 2019, ' Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data ', Acta Psychiatrica Scandinavica, vol. 140, no. 2, pp. 147-157 . https://doi.org/10.1111/acps.13061

OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first three days following admission co

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f99de7a240fc2a6d28c491fb9101e8e7
https://doi.org/10.1111/acps.13061