Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …

Abstract Machine learning techniques provide new methods to predict diagnosis and clinical
outcomes at an individual level. We aim to review the existing literature on the use of …

Synapse density is reduced in postmortem cortical tissue from schizophrenia patients, which
is suggestive of increased synapse elimination. Using a reprogrammed in vitro model of …

The Canadian Network for Mood and Anxiety Treatments (CANMAT) previously published
treatment guidelines for bipolar disorder in 2005, along with international commentaries and …

Objective A growing body of evidence has put forward clinical risk factors associated with
patients with mood disorders that attempt suicide. However, what is not known is how to …

Background Despite lithium being the most efficacious treatment for bipolar disorder, its use
has been decreasing at least in part due to concerns about its potential to cause significant …

Objectives Although lithium renal effects have been extensively investigated, prevalence
rates of chronic kidney disease (CKD) in lithium‐treated patients vary. Our aim was to …

Background Concerns about potential adverse effects of long-term exposure to lithium as a
mood-stabilizing treatment notably include altered renal function. However, the incidence of …

Big data analytics and machine learning: 2015 and beyond. - Abstract - Europe PMC Sign in |
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The widespread adoption of electronic health record (EHRs) in healthcare systems has
created a vast and continuously growing resource of clinical data and provides new …