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Mashup Score: 2Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions - 30 day(s) ago
Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.
Source: mental.jmir.orgCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 28
(Toronto, December 20, 2023) We are pleased to announce that JMIR Mental Health (JMH) has been accepted for inclusion in MEDLINE, which is the U.S. National Library of Medicine’s premier bibliographic database. JMH was previously already indexed in PubMed, but MEDLINE is a more selective subset of PubMed, consisting of the top 5200 biomedical journals, and indexing in MEDLINE also means that articles are now also indexed with NLM Medical Subject Headings (MeSH terms) and other metadata. Selection for
Source: mental.jmir.orgCategories: General Medicine News, Future of MedicineTweet-
RT @JohnTorousMD: Big news from us at JMIR Mental Health: https://t.co/CIVGBXp47D. As the journal expands, we are growing our editorial boa…
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Mashup Score: 4Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches - 9 month(s) ago
Background: Although suicide is a leading cause of death among children, the optimal approach for using health care data sets to detect suicide-related emergencies among children is not known. Objective: This study aimed to assess the performance of suicide-related International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and suicide-related chief complaint in detecting self-injurious thoughts and behaviors (SITB) among children compared with clinician chart review. The study also aimed to examine variations in performance by child sociodemographics and type of self-injury, as well as develop machine learning models trained on codified health record data (features) and clinician chart review (gold standard) and test model detection performance. Methods: A gold standard classification of suicide-related emergencies was determined through clinician manual review of clinical notes from 600 emergency department visits between 2015 and 2019 by childre
Source: mental.jmir.orgCategories: General Medicine News, Latest HeadlinesTweet-
Researchers develop machine learning models that could improve suicide-risk prediction among #children https://t.co/kIudbO3Ww7 https://t.co/nIJAqgyQhF
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Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions https://t.co/1Iw1lQjTL2 via @JohnTorousMD et al