Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data.

Aims: Patients visiting the emergency department (ED) or hospitalized for heart failure (HF) are at increased risk for subsequent adverse outcomes, however effective risk stratification remains challenging. We utilized a machine-learning (ML)-based approach to identify HF patients at risk of adverse outcomes after an ED visit or hospitalization using a large regional administrative healthcare data system.

Methods And Results: Patients visiting the ED or hospitalized with HF between 2002-2016 in Alberta, Canada were included.

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published.

Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF).

Development and validation of echocardiography-based machine-learning models to predict mortality.

Background: Echocardiography (echo) based machine learning (ML) models may be useful in identifying patients at high-risk of all-cause mortality.

Methods: We developed ML models (ResNet deep learning using echo videos and CatBoost gradient boosting using echo measurements) to predict 1-year, 3-year, and 5-year mortality. Models were trained on the Mackay dataset, Taiwan (6083 echos, 3626 patients) and validated in the Alberta HEART dataset, Canada (997 echos, 595 patients).

Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms.

The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason. Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (2007-2020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality.

Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia.

Background: Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis.

Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study.

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ.

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets.

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible - subsequent studies often fail to identify the same sets.

Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study.

The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require a large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data, such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset.

Factors associated with SARS-CoV-2 test positivity in long-term care homes: A population-based cohort analysis using machine learning.

Background: SARS-Cov-2 infection rates are high among residents of long-term care (LTC) homes. We used machine learning to identify resident and community characteristics predictive of SARS-Cov-2 infection.

Methods: We linked 26 population-based health and administrative databases to identify the population of all LTC residents tested for SARS-Cov-2 infection in Ontario, Canada.

Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning.

Background: Machine learning applications using neuroimaging provide a multidimensional, data-driven approach that captures the level of complexity necessary for objectively aiding diagnosis and prognosis in psychiatry. However, models learned from small training samples often have limited generalizability, which continues to be a problem with automated diagnosis of mental illnesses such as obsessive-compulsive disorder (OCD). Earlier studies have shown that features incorporating prior neurobiological knowledge of brain function and combining brain parcellations from various sources can potentially improve the overall prediction.

Learning the structure-activity relationship (SAR) of the Wittig reaction from genetically-encoded substrates.

The Wittig reaction can be used for late stage functionalization of proteins and peptides to ligate glycans, pharmacophores, and many other functionalities. In this manuscript, we modified 160 000 N-terminal glyoxaldehyde peptides displayed on phage with the Wittig reaction by using a biotin labeled ylide under conditions that functionalize only 1% of the library population. Deep-sequencing of the biotinylated and input populations estimated the rate of conversion for each sequence.

Predictors of mortality among long-term care residents with SARS-CoV-2 infection.

Background: While individuals living in long-term care (LTC) homes have experienced adverse outcomes of SARS-CoV-2 infection, few studies have examined a broad range of predictors of 30-day mortality in this population.

Methods: We studied residents living in LTC homes in Ontario, Canada, who underwent PCR testing for SARS-CoV-2 infection from January 1 to August 31, 2020, and examined predictors of all-cause death within 30 days after a positive test for SARS-CoV-2. We examined a broad range of risk factor categories including demographics, comorbidities, functional status, laboratory tests, and characteristics of the LTC facility and surrounding community were examined.

Pars Triangularis Volume Asymmetry and Schneiderian First Rank Symptoms in Antipsychotic-naïve Schizophrenia.

Objective: Schizophrenia is a disorder of language and self, with first-rank symptoms (FRS) as one of the predominant features in a subset of patients. Abnormal language lateralization is hypothesized to underlie the neurobiology of FRS in schizophrenia. The role of Broca's area with its right-hemispheric counterpart, consisting of pars triangularis (PTr) and pars opercularis (POp) of the inferior frontal gyrus in FRS is undetermined.

Antisaccade task performance in obsessive-compulsive disorder and its clinical correlates.

Objective: Obsessive-Compulsive Disorder (OCD) is characterized by abnormalities in the cortico-striato-thalamo-cortical (CSTC) circuitry of the brain. Antisaccade eye movement tasks measure aspects of the voluntary control of behaviour that are sensitive to CSTC circuitry dysfunction.

Method: In this study, we examined antisaccade eye movement parameters of OCD patients in comparison with healthy controls (HC).

Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives.

Recently, we developed a machine-learning algorithm "EMPaSchiz" that learns, from a training set of schizophrenia patients and healthy individuals, a model that predicts if a novel individual has schizophrenia, based on features extracted from his/her resting-state functional magnetic resonance imaging. In this study, we apply this learned model to first-degree relatives of schizophrenia patients, who were found to not have active psychosis or schizophrenia. We observe that the participants that this model classified as schizophrenia patients had significantly higher "schizotypal personality scores" than those who were not.

Leukocyte mitochondrial DNA copy number in schizophrenia.

Objective: Schizophrenia is a complex neuropsychiatric disorder with significant genetic predisposition. In a subset of schizophrenia patients, mitochondrial dysfunction could be explained by the genomic defects like mitochondrial DNA Copy Number Variations, which are considered as a sensitive index of cellular oxidative stress. Given the high energy demands for neuronal functions, altered Mitochondrial DNA copy number (mtDNAcn) and consequent impaired mitochondrial physiology would significantly influence schizophrenia pathogenesis.

Differential impact of interleukin-6 promoter gene polymorphism on hippocampal volume in antipsychotic-naïve schizophrenia patients.

Background: Differential susceptibility model hypothesizes that a genotype need not be unfavorable all the time as postulated in stress-diathesis model but can be beneficial in a supportive context. Single-nucleotide polymorphism (SNP) (rs18000795) within the promoter region of interleukin-6 (IL-6) gene was earlier noted to have a differential susceptibility on hippocampal volume in schizophrenia (SCZ).

Materials And Methods: We examined antipsychotic-naïve/free SCZ patients ( = 35) in comparison with healthy controls ( = 68).

Tolerance of transcranial direct current stimulation in psychiatric disorders: An analysis of 2000+ sessions.

Transcranial direct current stimulation (tDCS), a non-invasive, neuromodulatory technique, is being increasingly applied to several psychiatric disorders. In this study, we describe the side-effect profile of repeated tDCS sessions (N = 2005) that were administered to 171 patients (156 adults and 15 adolescents) with different psychiatric disorders [schizophrenia [N = 109], obsessive-compulsive disorder [N = 28], alcohol dependence syndrome [N = 13], mild cognitive impairment [N = 10], depression [N = 6], dementia [N = 2] and other disorders [N = 3]]. tDCS was administered at a constant current strength of 2 mA with additional ramp-up and ramp-down phase of 20 s each at the beginning and end of the session, respectively.

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning.

In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases.

Neuro-hemodynamic endophenotypes of emotional interference in OCD: fMRI study using emotion counting stroop task.

Background: We sought to examine the endophenotype pattern of neuro-hemodynamic substrates of emotion counting Stroop (ecStroop) paradigm in patients with OCD, their unaffected siblings [first degree relatives-FDR] and healthy controls (HC).

Methods: OCD patients (medication naïve)[N = 16], their unaffected siblings(FDR)[N = 16] and HC [N = 24] were compared using an established ecStroop paradigm in a 3-Tesla fMRI. The relative BOLD signals corresponding to the three types of conditions (neural words-N, words with negative emotional salience-E and words with salience for OCD-O) were examined in the apriori hypothesized brain regions.

Telomere length and its association with hippocampal gray matter volume in antipsychotic-naïve/free schizophrenia patients.

Accelerated ageing processes are postulated to underlie schizophrenia pathogenesis. This postulate is supported by observations of reduced telomere length in schizophrenia patients. Hippocampus, one of the most important brain regions implicated in schizophrenia, is shown to atrophy at a faster rate in aging.

Impact of antipsychotic treatment on methylation status of Interleukin-6 [IL-6] gene in Schizophrenia.

Immunopathogenesis of schizophrenia has emerged as one of the predominant research paradigms in recent times. Based on the altered serum levels as well as gene expression, IL-6 has been considered as a peripheral biomarker of schizophrenia. However, the precise mechanism underlying the altered expression of IL6 in schizophrenia is inadequately known.