Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD).

Signs and symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) are present at preschool ages and often not identified for early intervention. We aimed to use machine learning to detect ADHD early among kindergarten-aged children using population-level administrative health data and a childhood developmental vulnerability surveillance tool: Early Development Instrument (EDI). The study cohort consists of 23,494 children born in Alberta, Canada, who attended kindergarten in 2016 without a diagnosis of ADHD.

Six-year (2016-2022) longitudinal patterns of mental health service utilization rates among children developmentally vulnerable in kindergarten and the COVID-19 pandemic disruption.

Introduction: In the context of the COVID-19 pandemic, it becomes important to comprehend service utilization patterns and evaluate disparities in mental health-related service access among children.

Objective: This study uses administrative health records to investigate the association between early developmental vulnerability and healthcare utilization among children in Alberta, Canada from 2016 to 2022.

Methods: Children who participated in the 2016 Early Development Instrument (EDI) assessment and were covered by public Alberta health insurance were included (N = 23 494).

Dual-species proteomics and targeted intervention of animal-pathogen interactions.

Introduction: Host-microbe interactions are important to human health and ecosystems globally, so elucidating the complex host-microbe interactions and associated protein expressions drives the need to develop sensitive and accurate biochemical techniques. Current proteomics techniques reveal information from the point of view of either the host or microbe, but do not provide data on the corresponding partner. Moreover, it remains challenging to simultaneously study host-microbe proteomes that reflect the direct competition between host and microbe.

Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study.

Background: Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders.

Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report.

Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment. Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram.

The emergence of all-or-none retrieval of chunks in verbal serial recall.

People often subdivide a list into smaller pieces, called chunks. Some theories of serial recall assume memories are stored hierarchically, with all-or-none retrieval of chunks, but most mathematical models avoid hierarchical assumptions. Johnson (Journal of Verbal Learning and Verbal Behavior, 8(6), 725-731, 1969) found steep drops in errors following correct recalls (transitional-error probabilities) within putative chunks during multi-trial letter-list learning, and viewed this as evidence for all-or-none retrieval.

Practical pathway for the management of depression in the workplace: a Canadian perspective.

Major depressive disorder (MDD) and other mental health issues pose a substantial burden on the workforce. Approximately half a million Canadians will not be at work in any week because of a mental health disorder, and more than twice that number will work at a reduced level of productivity (presenteeism). Although it is important to determine whether work plays a role in a mental health condition, at initial presentation, patients should be diagnosed and treated per appropriate clinical guidelines.

Associations between Differential Aging and Lifestyle, Environment, Current, and Future Health Conditions: Findings from Canadian Longitudinal Study on Aging.

Introduction: An aging population will bring a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors, and chronic disease conditions.

Methods: We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA).

Response trajectories during escitalopram treatment of patients with major depressive disorder.

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories.

Prediction of depression onset risk among middle-aged and elderly adults using machine learning and Canadian Longitudinal Study on Aging cohort.

Background: Early identification of the middle-aged and elderly people with high risk of developing depression disorder in the future and the full characterization of the associated risk factors are crucial for early interventions to prevent depression among the aging population.

Methods: Canadian Longitudinal Study on Aging (CLSA) has collected comprehensive information, including psychological scales and other non-psychological measures, i.e.

Screening for Adulthood ADHD and Comorbidities in a Tertiary Mental Health Center Using EarlyDetect: A Machine Learning-Based Pilot Study.

Screening for adult Attention-Deficit/Hyperactivity Disorder (ADHD) and differentiating ADHD from comorbid mental health disorders remains to be clinically challenging. A screening tool for ADHD and comorbid mental health disorders is essential, as most adult ADHD is comorbid with several mental health disorders. The current pilot study enrolled 955 consecutive patients attending a tertiary mental health center in Canada and who completed EarlyDetect assessment, with 45.

Systematic Review of Psychiatric Adverse Effects Induced by Chloroquine and Hydroxychloroquine: Case Reports and Population Studies.

Objective: To perform a systematic review on the psychiatric adverse effects of chloroquine (CQ) and hydroxychloroquine (HCQ); to summarize what is known about psychiatric adverse effects of these drugs; to compare clinical trials, populational studies, and case report studies; and to increase awareness of the potential psychiatric adverse effects of these drugs.

Data Sources: A literature search of PubMed, Scopus, and Web of Science was performed to identify manuscripts published between December 1962 and June 2022. Search terms included CQ, HCQ, psychiatry, psychosis, depression, anxiety, bipolar disorder, delirium, and psychotic disorders.

Individualized Prospective Prediction of Opioid Use Disorder.

Objective: Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases.

Individualized identification of sexual dysfunction of psychiatric patients with machine-learning.

Sexual dysfunction (SD) is prevalent in patients with mental health disorders and can significantly impair their quality of life. Early recognition of SD in a clinical setting may help patients and clinicians to optimize treatment options of SD and/or other primary diagnoses taking SD risk into account and may facilitate treatment compliance. SD identification is often overlooked in clinical practice; we seek to explore whether patients with a high risk of SD can be identified at the individual level by assessing known risk factors via a machine learning (ML) model.

Judgments of alphabetical order and mechanisms of congruity effects.

The congruity effect is a highly replicated feature of comparative judgments, and has been recently found in memory judgments of relative temporal order. Specifically, asking "Which came earlier?" versus "Which came later?" facilitates response times and sometimes error rates on judgments toward the beginning or end of the list, respectively. This suggests memory judgments of relative temporal order may be part of a broader class of comparative judgments.

Depression screening using a non-verbal self-association task: A machine-learning based pilot study.

Background: Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to supplement verbal screening. Differential self- and emotion-processing in depression patients were previously reported by non-verbal behavioural assessments, corroborated by neuroimaging findings of distinct neuroanatomical markers.

Rapid detection of microorganisms in a fish infection microfluidics platform.

Inadequate access to clean water is detrimental to human health and aquatic industries. Waterborne pathogens can survive prolonged periods in aquatic bodies, infect commercially important seafood, and resist water disinfection, resulting in human infections. Environmental agencies and research laboratories require a relevant, portable, and cost-effective platform to monitor microbial pathogens and assess their risk of infection on a large scale.

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study.

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ).

Validation of a 22-Gene Genomic Classifier in Patients With Recurrent Prostate Cancer: An Ancillary Study of the NRG/RTOG 9601 Randomized Clinical Trial.

Importance: Decipher (Decipher Biosciences Inc) is a genomic classifier (GC) developed to estimate the risk of distant metastasis (DM) after radical prostatectomy (RP) in patients with prostate cancer.

Objective: To validate the GC in the context of a randomized phase 3 trial.

Design, Setting, And Participants: This ancillary study used RP specimens from the phase 3 placebo-controlled NRG/RTOG 9601 randomized clinical trial conducted from March 1998 to March 2003.

Temporal grouping and direction of serial recall.

When lists are presented with temporal pauses between groups of items, participants' response times reiterate those pauses. Accuracy is also increased, especially at particular serial positions. By comparing forward with backward serial recall, we tested whether the influence of temporal grouping is primarily a function of serial position or output position.

Outcomes After Recurrent Intentional Methanol Exposures Not Treated With Alcohol Dehydrogenase Inhibitors Or Hemodialysis.

Background: Relying on a treatment threshold for methanol poisoning of 20 mg/dL (6.2 mmol/L) as a stand-alone criterion may lead to unnecessary and invasive treatment because it is likely too conservative, especially for patients with repeated, intentional methanol exposures.

Objective: We investigated how often patients with recurrent intentional methanol exposures above this threshold developed biochemical or overt clinical toxicity despite not being treated with either an alcohol dehydrogenase inhibitor (ADHi) or hemodialysis.