Exploring assisted dying policies for mature minors: A cross jurisdiction comparison of the Netherlands, Belgium & Canada.

Medical Assistance in Dying (MAID) was decriminalized in Canada in 2016 for individuals 18 years or older who met eligibility criteria. Currently, individuals younger than 18 years are legally permitted to access an assisted death in the Netherlands and Belgium, but not in Canada. To-date, no work has compared factors shaping the policy processes and outcomes in these three countries.

Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided.

Purpose: In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients.

Methods: Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course.

A Primer on Artificial Intelligence for Healthcare Administrators.

Healthcare administrators steer their organizations' strategic direction with an emphasis on quality, value and efficiency, aiming to improve patient outcomes and ensure operational sustainability. Artificial intelligence (AI) has become a transformative force in healthcare in the past decade, with Canadian health systems and research institutions investing in AI solutions to address critical healthcare challenges. This primer delivers a fundamental guide to essential AI concepts in healthcare and provides practical guidance to prepare organizations for AI readiness.

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.

While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range.

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review.

Background: Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment.

Maximizing the value of patient and public involvement in the digital health co-design process: A qualitative descriptive study with design leaders and patient-public partners.

Digital health interventions have enormous potential to support patients and the public in achieving their health goals. Nonetheless, many digital health interventions are failing to effectively engage patients and the public. One solution that has been proposed is to directly involve patients and the public in the design process of these digital health interventions.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets.

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE.

Optimizing Patient Record Linkage in a Master Patient Index Using Machine Learning: Algorithm Development and Validation.

Background: To provide quality care, modern health care systems must match and link data about the same patient from multiple sources, a function often served by master patient index (MPI) software. Record linkage in the MPI is typically performed manually by health care providers, guided by automated matching algorithms. These matching algorithms must be configured in advance, such as by setting the weights of patient attributes, usually by someone with knowledge of both the matching algorithm and the patient population being served.

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.

Purpose: This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system.

Methods: Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs.

Nailfold capillaroscopy and deep learning in diabetes.

Objective: To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and its complications.

Research Design And Methods: Nailfold video capillaroscopy was performed in 120 adult patients with and without type 1 or type 2 diabetes, and with and without cardiovascular disease. Nailfold images were analyzed using convolutional neural networks, a deep learning technique.

Machine learning for detecting centre-level irregularities in randomized controlled trials: A pilot study.

Centralized statistical monitoring is sometimes employed as an alternative to onsite monitoring for randomized control trials. Current central monitoring methods have limitations, in that they are relatively resource intensive and do not necessarily generalize to studies where an irregularity pattern has not been observed before. Machine learning has been effective in detecting irregularities in industries such as finance and manufacturing, but to date none have been applied to clinical trials.

Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study.

Background: Health coaching is an emerging intervention that has been shown to improve clinical and patient-relevant outcomes for type 2 diabetes. Advances in artificial intelligence may provide an avenue for developing a more personalized, adaptive, and cost-effective approach to diabetes health coaching.

Objective: We aim to apply Q-learning, a widely used reinforcement learning algorithm, to a diabetes health-coaching data set to develop a model for recommending an optimal coaching intervention at each decision point that is tailored to a patient's accumulated history.

Integrating Behavioral Science and Design Thinking to Develop Mobile Health Interventions: Systematic Scoping Review.

Background: Mobile health (mHealth) interventions are increasingly being designed to facilitate health-related behavior change. Integrating insights from behavioral science and design science can help support the development of more effective mHealth interventions. Behavioral Design (BD) and Design Thinking (DT) have emerged as best practice approaches in their respective fields.

A machine learning-based clinical decision support algorithm for reducing unnecessary coronary angiograms.

Background: Conventional clinical risk scores and diagnostic algorithms are proving to be suboptimal in the prediction of obstructive coronary artery disease, contributing to the low diagnostic yield of invasive angiography. Machine learning could help better predict which patients would benefit from invasive angiography vs other noninvasive diagnostic modalities.

Objective: To reduce patient risk and cost to the healthcare system by improving the diagnostic yield of invasive coronary angiography through optimized outpatient selection.

The Unspeakable Nature of Death & Dying During Childhood: A Silenced Phenomenon in Pediatric Care.

In pediatric settings, the concept of hope is frequently positioned as a fundamental aspect of care and at odds with the possibility and proximity of death. This arguably fosters silence about death and dying in childhood despite evidence indicating the benefits of open communication at the end of life. In this paper, we describe the unspeakable nature of death and dying in childhood, including its conceptual and clinical causes and dimensions, its persistence, and the associated challenges for children and youth facing critical illnesses, their families, and society.

GWAS and ExWAS of blood mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia.

Background: Mitochondrial DNA copy number (mtDNA-CN) is an accessible blood-based measurement believed to capture underlying mitochondrial (MT) function. The specific biological processes underpinning its regulation, and whether those processes are causative for disease, is an area of active investigation.

Methods: We developed a novel method for array-based mtDNA-CN estimation suitable for biobank-scale studies, called 'automatic mitochondrial copy (AutoMitoC).

Continuous Noninvasive Remote Automated Blood Pressure Monitoring With Novel Wearable Technology: A Preliminary Validation Study.

Background: Wearable continuous monitoring biosensor technologies have the potential to transform postoperative care with early detection of impending clinical deterioration.

Objective: Our aim was to validate the accuracy of Cloud DX Vitaliti continuous vital signs monitor (CVSM) continuous noninvasive blood pressure (cNIBP) measurements in postsurgical patients. A secondary aim was to examine user acceptance of the Vitaliti CVSM with respect to comfort, ease of application, sustainability of positioning, and aesthetics.

The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach.

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling.

Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology.

Many clinicians remain wary of machine learning because of longstanding concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpretability in predictive models can undermine trust in those models, especially in health care, in which so many decisions are- literally-life and death issues.

Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.

Background: Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.

Extracting Clinical Features From Dictated Ambulatory Consult Notes Using a Commercially Available Natural Language Processing Tool: Pilot, Retrospective, Cross-Sectional Validation Study.

Background: The increasing adoption of electronic health records (EHRs) in clinical practice holds the promise of improving care and advancing research by serving as a rich source of data, but most EHRs allow clinicians to enter data in a text format without much structure. Natural language processing (NLP) may reduce reliance on manual abstraction of these text data by extracting clinical features directly from unstructured clinical digital text data and converting them into structured data.

Objective: This study aimed to assess the performance of a commercially available NLP tool for extracting clinical features from free-text consult notes.