Importance:Existing evidence shows that the environment is a risk factor in preterm birth. However, more evidence is needed about the association between environmental exposures and outcomes of preterm birth. Objective:To examine the association of antepartum exposure to individual and combined indices of greenness, air pollution, and extreme temperatures with outcomes of preterm infants. Design, Setting, and Participants:This cohort study used linked national databases of the Canadian Neonatal Network and Canadian Urban Environmental Health Research Consortium. The cohort consisted of infants born at 22 to 28 weeks plus 6 days' gestation between January 1, 2010, and December 31, 2020, and treated in tertiary neonatal intensive care units (NICUs) across Canada. Data were analyzed between May 1 and September 15, 2024. Exposure:Indices summarizing levels of greenness, air pollutants, and ambient temperature at mothers' residential postal code at birth. Main Outcome and Measure:Infant survival without major morbidity (SWMM) assessed at death or discharge from the NICU. Logistic regression models estimated associations between individual and combination of indices with SWMM. Results:A total of 14 748 infants (7965 males [54.0%]; mean [SD] gestational age, 26.1 [1.6] weeks; median [IQR] birth weight, 890 [720-1090] g) were included. The rate of SWMM was 32.1% (4737 of 14 748). Infants born to mothers who were exposed to high ozone levels had lower odds of SWMM vs infants with mothers exposed to low ozone levels (adjusted odds ratio [AOR], 0.83; 95% CI, 0.74-0.95). Infants with antepartum exposure to low temperature combined with either high levels of ozone (AOR, 0.76; 95% CI, 0.60-0.95) or low levels of greenness (AOR, 0.77; 95% CI, 0.60-0.99) or both low levels of greenness and high levels of ozone (AOR, 0.58; 95% CI, 0.43-0.77) had lower odds of SWMM than infants with mothers who were not exposed to these risk factors. Conclusions and Relevance:In this cohort study, antepartum exposure to high ozone and combined low temperatures and low levels of greenness or high ozone were associated with lower odds of SWMM in preterm infants. These findings suggest that the health outcomes of antepartum environmental exposure extend to neonatal outcomes of preterm infants.

Antimicrobial resistance threatens healthcare sustainability and motivates low-cost computational discovery of antimicrobial peptides (AMPs). De novo peptide generation must optimize antimicrobial activity and safety through low predicted toxicity, but likelihood-trained generators do not enforce these goals explicitly. We introduce ProDCARL, a reinforcement-learning alignment framework that couples a diffusion-based protein generator (EvoDiff OA-DM 38M) with sequence property predictors for AMP activity and peptide toxicity. We fine-tune the diffusion prior on AMP sequences to obtain a domain-aware generator. Top-k policy-gradient updates use classifier-derived rewards plus entropy regularization and early stopping to preserve diversity and reduce reward hacking. In silico experiments show ProDCARL increases the mean predicted AMP score from 0.081 after fine-tuning to 0.178. The joint high-quality hit rate reaches 6.3% with pAMP >0.7 and pTox <0.3. ProDCARL maintains high diversity, with 1-mean pairwise identity equal to 0.929. Qualitative analyses with AlphaFold3 and ProtBERT embeddings suggest candidates show plausible AMP-like structural and semantic characteristics. ProDCARL serves as a candidate generator that narrows experimental search space, and experimental validation remains future work.

Metastatic castration-resistant prostate cancer (mCRPC) is a highly aggressive disease with poor prognosis and heterogeneous treatment response. In this work, we developed and externally validated a visit-level 180-day mortality risk model using longitudinal data from two Phase III cohorts (n=526 and n=640). Only visits with observable 180-day outcomes were labeled; right-censored cases were excluded from analysis. We compared five candidate architectures: Long Short-Term Memory, Gated Recurrent Unit (GRU), Cox Proportional Hazards, Random Survival Forest (RSF), and Logistic Regression. For each dataset, we selected the smallest risk-threshold that achieved an 85

Algorithms that support screening, triage, and treatment decisions depend on training data drawn from patient populations. Limited access to patient-level records across institutions and jurisdictions can reduce representation and contribute to uneven model performance across populations. Canada’s federated health system, where provinces and territories manage separate datasets and privacy regimes, limits multicenter medical AI research. We conducted a scoping review to map how Canadian researchers share patient-level data in multicenter medical AI collaborations. We searched PubMed, IEEE Xplore, ACM Digital Library, Scopus, and Web of Science from 2018 to February 2025 and implemented a human-in-the-loop large language model process to support screening and extraction, with reviewer validation. Among 3100 included studies, 160 reported multicenter patient-level data collection. Centralized pooling dominated this subset, with 95% of studies using centralized storage and 5% (n = 8) reporting decentralized approaches, including federated learning, sequential model transfer, and distributed feature sharing. Governance requirements were frequently described as multi-site and sequential, and 81.8% of multicenter collaborations reported parallel ethics approvals from three or more institutional review boards. Only one decentralized collaboration operated entirely within Canada. International partnerships comprised 80% of multicenter studies, and many cohorts included non-Canadian sites or non-Canadian data. Our findings support adoption of distributed model development protocols and interoperable governance that limit central pooling while enabling consistent training, validation, and reporting across sites, as only 1 of 160 multicenter studies reported a decentralized approach with Canadian patient data only.

A disease label does not always define a stable language model task. We test this problem in diabetes, a common and stigmatized condition where Type 1 and Type 2 communities share a diagnosis but use different cultural language for daily care and public judgment. We built a patient discourse corpus with a manually annotated reference set of 1526 posts from condition specific Type 1 and Type 2 forums, supported by 1386 additional texts from patient interviews, forum posts, and published patient narratives for model development. Our lexical and embedding analyses identified distinct diabetes language patterns across the two communities. A stigma detector trained on Type 2 discourse lost sensitivity when applied to Type 1 stigma posts, with F1 falling from 0.91 in domain to 0.43 out of domain. Direct LLM prompting showed that stigma detection varied with prompt context, with generic prompts missing rare stigma signals and calibrated prompts recovering positive class sensitivity. We then introduced a culture sensitive calibration and adjudication framework that does not hard code diabetes type into prediction. Prompt calibration raised GPT-5.4 Macro F1 from 0.515 to 0.776, and importance weighted adjudication improved downstream binary and subtype Macro F1 to 0.767 and 0.368. Our results show that a shared diagnosis does not define a shared language model task, and that culture sensitive calibration with adjudicated expansion can improve the validity of inference from patient language.

Routine H&E histopathology slides are widely available for breast cancer patients, whereas transcriptomic profiling remains more costly and less routinely accessible. This gap limits the scalability of molecularly informed precision oncology. We propose a diagnosis-label-free contrastive learning framework that aligns tumor-region H&E histopathology features with matched bulk RNA-seq transcriptomic profiles in a shared latent space. Using paired histopathology patches and transcriptomic profiles from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort, the model learns patient-level image–transcriptome correspondences through a dual-encoder InfoNCE objective. We evaluate the learned space through cross-modal retrieval of gene expression, MSigDB Hallmark pathway activity, and PAM50 molecular subtype information. The model retrieves modest but biologically meaningful transcriptomic and pathway-level signals from histopathology alone, including concordance for selected cancer-relevant pathways. These results provide a reproducible proof of concept for image-based transcriptomic profiling from routine histopathology, while further benchmarking, external validation, and clinical utility assessment are required before clinical use.

Patient satisfaction is a central measure of high-performing healthcare systems, yet real-world evaluations at scale remain challenging. In this study, we analyzed 122,194 de-identified patient reviews from 45 Ontario general hospitals between January 2015 and July 2022. We applied a natural language processing (NLP) pipeline using a clinical named entity recognition (NER) model fine-tuned on biomedical literature to extract references to diseases, symptoms, and medical procedures from patient reviews. Geospatial analysis was conducted to examine sentiment patterns based on regional census data related to low-income status and visible-minority composition. Our primary objective was to investigate how the COVID-19 pandemic influenced patient satisfaction trends, with a specific focus on clinical units and hospitals serving marginalized populations. We assessed changes in the proportion of positive comments across time periods and socioeconomic groups using multivariate logistic regression. Our findings show that over 80% of the hospitals studied had fewer than 50% positive reviews, highlighting possible systemic gaps in patient needs. Interestingly, the proportion of negative reviews decreased during the COVID-19 pandemic, suggesting possible changes in patient expectations or increased appreciation for healthcare workers. However, certain units, such as dentistry and radiology, experienced more negative ratings as a proportion of their total reviews. 'Anxiety' emerged as a recurrent concern in negative reviews, especially during the start of the pandemic, pointing to the growing awareness of mental health needs. Based on our geospatial analysis, hospitals located in regions with higher percentages of visible minority and low-income populations initially saw higher positive review proportions before COVID-19, but this trend reversed after 2020. Our statistical models confirmed that these shifts were significant, particularly for low-income-serving hospitals. Collectively, these results demonstrate how large-scale unstructured data can identify fundamental drivers of patient satisfaction, while underscoring the urgent need for adaptive strategies to address anxiety and combat systemic inequities.

In January 2023, British Columbia (BC) launched Canada’s first drug decriminalization pilot in response to persistently high opioid-related deaths. This initiative removed criminal penalties for possession of up to 2.5 grams of certain illicit drugs to reduce stigma and increase service use. Social media platforms such as Reddit provide a real-time window into public perceptions and misconceptions about such policies. We analyzed 12,537 Reddit comment records posted between January 2023 and October 2024 from subreddits discussing decriminalization in British Columbia. A zero-shot large language model applied a multi-label taxonomy with four misconception classes and one no-claim class. Expert review of 547 comments with model disagreement supported selection of the primary classifier for analysis. We tracked monthly misconception prevalence and used subreddit-week reply premiums and lattice clustering to detect engagement bursts. Negative binomial models quantified reply differences across periods, regions, and comment-level harm reduction references. GPT-4o assigned at least one substantive misconception label to 2,457 comment records ( 19.60% ). Label 5 was assigned to 10,080 records ( 80.40% ), consistent with a conservative taxonomy that separates checkable policy claims from opinion, sarcasm, anecdote, and moral judgment. After the May 2024 amendment that restricted possession in public spaces, legal status and enforcement misconceptions increased. Legal or enforcement misconception records had a higher adjusted reply-rate estimate than other records, although the main effect had limited precision (IRR 1.13 , 95

ObjectiveTo develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis.Materials and methodsWe designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), and fairness modules. Bias mitigation was addressed through the integration of three complementary approaches: adversarial debiasing, Hard Debiasing, and Iterative Null-space Projection (INLP). Multiple BERT-based models (DistilBERT, BioBERT, RoBERTa-base, BERT-base-uncased) were trained and evaluated under varying hyperparameters and fairness/adversarial loss configurations. Model performance was assessed using accuracy, F1, recall, precision, AUC, Equalized Odds (EOD), and Word Embedding Association Test (WEAT) metrics.ResultsAdversarial loss ([Formula: see text]) consistently decreased model performance across accuracy, F1, precision, and recall. In contrast, Hard Debiasing and INLP improved WEAT scores while preserving or enhancing other metrics, with INLP yielding the best overall performance. Specifically, INLP with fairness loss improved EOD by 14%, gender WEAT scores by 15%, and achieved slight gains for ethnicity and socioeconomic WEAT scores. The best model achieved accuracy of 0.856, F1 score of 0.812, recall of 0.798, and precision of 0.829. The key theme analysis module identified 82% of expert-labeled themes, though 21% of patient comments lacked expert labels for valence or related attributes.DiscussionOur results demonstrate the trade-offs between fairness and performance in bias mitigation strategies. While adversarial debiasing reduced predictive accuracy, INLP and Hard Debiasing improved fairness without significant degradation in task performance. Gender bias was easier to mitigate than multi-class sensitive attributes such as ethnicity and income. This difference indicates a need for fairness techniques designed for multi-class sensitive attributes.ConclusionThis work presents an NLP pipeline for patient feedback analysis with multiple debiasing strategies. This pipeline improves the fairness and accuracy of insights from unstructured patient reviews and supports inclusive patient-centered care.

Type 2 diabetes has become an urban epidemic influenced by neighbourhood environments. However, conventional risk models focusing solely on individual factors fail to account for these neighbourhood influences and often require detailed patient data that may not be available. To address this gap, we developed an integrated approach combining machine learning and causal inference to map type 2 diabetes risk at the neighbourhood level. Using demographic, health, and socioeconomic data from 1,149 Census Tracts (CTs; the neighbourhood unit in this study) in a large metropolitan region, we trained seven machine learning models to identify neighbourhoods with high diabetes prevalence. Although neighbourhood-level diabetes data were available for this study area, our model's high predictive accuracy on external validation data (area under the curve (AUC) = 0.95), particularly from a distinct geographical region, suggests potential utility for predicting diabetes risk in other Canadian regions or elsewhere where such data are unavailable, provided comparable covariates are available and the model is locally retrained and validated using spatially aware procedures. The top models achieved high recall ([Formula: see text]) and AUC up to 0.96 on test data, indicating accurate identification of high-risk neighbourhoods with few missed high-risk areas. Survey-derived neighbourhood health indicators, including obesity rate, physical inactivity, and median age were strong predictors of diabetes prevalence. We then applied a Causal Forest approach to estimate conditional average treatment effects (CATE, τ) for selected potentially modifiable factors and summarized the results with the mean [Formula: see text]. Higher work stress ([Formula: see text]) and daily smoking ([Formula: see text]) were moderately associated with increased risk, whereas better mental health ([Formula: see text]) was protective, highlighting mental health as a priority for further evaluation, especially in neighbourhoods predicted to have high diabetes prevalence. These findings could help identify modifiable neighbourhood-level factors for local prevention efforts and inform equity-oriented planning in diverse urban populations. Prospective or quasi-experimental studies are needed to evaluate intervention effects. Our integrated machine-learning and causal framework lays the groundwork for precision public health, suggesting that modifiable neighbourhood factors may indicate diabetes risk when patient-level data are scarce. Furthermore, the pipeline is conceptually adaptable to other chronic diseases influenced by social and environmental determinants and may inform targeted prevention beyond type 2 diabetes, contingent on disease-specific feature sets and external validation.

Precision medicine runs on patient-level data. In Canada, most patient-level AI collaborations are international, and many draw on datasets housed outside the country. We mapped the landscape of Canadian healthcare-AI collaborations using patient-level data and found that privacy-preserving data-sharing practices are extremely rare. Using a custom large language model pipeline, we screened 245,886 articles (2018–Feb 2025) and identified 3,100 relevant studies. The main dataset drawn from PubMed after a pilot assessment against IEEE Xplore, ACM Digital Library, Scopus, and Web of Science indicated equivalent coverage for our eligibility criteria. We identified trends in domestic and international collaborations and examined implications for data sharing, privacy, and interoperability. Among 3,100 studies, only 35.8% were conducted solely by Canadian institutions and nearly two thirds involved international partners. Imaging data were the most common modality. Alarmingly, out of 160 multi-site collaborations, just 5% (8 studies) used any privacy-preserving method, and only one of those was a fully within-Canada collaboration using Canadian patients' data. Canada's slow adoption of privacy-preserving collaboration technologies is limiting the country's ability to fully leverage AI in healthcare. Without immediate investment in secure, decentralized data sharing infrastructure, Canada will fall behind in health AI. Prioritizing such infrastructure is critical to enable innovation and to ensure Canadian patient data benefit Canadian patients.

Diabetes mellitus remains a major global health burden, causing an estimated 3.4 million deaths in 2024 and highlighting the need for accurate early identification of individuals at risk of developing type 2 diabetes (T2D). Electronic health records (EHRs) provide longitudinal clinical trajectories, yet many predictive frameworks fail to capture short-, intermediate-, and long-term temporal patterns or incorporate clinically validated metabolic biomarkers. This study introduces a hybrid deep learning framework that integrates hierarchical temporal modeling with clinically engineered predictors for early T2D risk estimation. The approach includes data preprocessing, temporal sequencing, and the incorporation of derived biomarkers such as triglyceride-to–high-density lipoprotein cholesterol ratio (TG/HDL-C), low-density lipoprotein to high-density lipoprotein cholesterol ratio (LDL/HDL-C), total cholesterol to high-density lipoprotein cholesterol ratio (TC/HDL-C), very low-density lipoprotein (VLDL), obesity status, and prediabetes indicators. A multilevel convolutional neural network (CNN) extracts low-, mid-, and high-level temporal features, which are processed in parallel by long short-term memory (LSTM) modules to capture multi-scale temporal dependencies. The fused temporal and biochemical representations form a unified CNN–LSTM architecture that is evaluated using standard classification metrics. Experiments conducted on 19,218 patients and 368,790 clinical visits from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) achieved 93.2% accuracy, 75.7% sensitivity, 98.8% specificity, and an 84.4% F1 score, outperforming bidirectional long short-term memory (Bi-LSTM), support vector machine (SVM), k-nearest neighbor (KNN), and baseline CNN–LSTM models. Feature importance analysis identified fasting blood sugar (FBS), glycated hemoglobin (HbA1c), and lipid ratios as the strongest predictors. By combining temporal representation learning with clinically grounded biomarkers, the proposed framework provides an interpretable, scalable, and robust foundation for early diabetes risk prediction and can be extended to other chronic diseases characterized by longitudinal EHR data. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Yes ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The individual-level datasets used in our study had been de-identified prior to our use of them in this study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The dataset analyzed in this study was accessed through the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) under a formal data-sharing agreement. Due to privacy and confidentiality regulations, the data cannot be made publicly available. Researchers who meet the criteria for access to sensitive health data may obtain it directly from CPCSSN at https://www.cpcssn.ca.

Online gambling platforms have transformed the gambling landscape, offering unprecedented accessibility and personalized experiences. However, these same characteristics have increased the risk of gambling-related harm, affecting individuals, families, and communities. Structural factors, including targeted marketing, shifting social norms, and gaps in regulation, further complicate the challenge. This narrative review examines how artificial intelligence, particularly multimodal generative models and foundation technologies, can address these issues by supporting prevention, early identification, and harm-reduction efforts. We detail applications such as synthetic data generation to overcome research barriers, customized interventions to guide safer behaviors, gamified tools to support recovery, and scenario modeling to inform effective policies. Throughout, we emphasize the importance of safeguarding privacy and ensuring that technological advances are responsibly aligned with public health objectives.

Opioid decriminalization has taken on renewed urgency in regions grappling with high mortality and healthcare costs. Traditional assessments often focus on legal or epidemiological data, leaving gaps in understanding how the public actually perceives and reacts to such policies. This paper introduces an AI-driven approach that applies Mistral, a Large Language Model (LLM), to a corpus of over 22,000 Reddit comments discussing British Columbia's decriminalization policy. Our method uses zero-shot classification to track shifts in sentiment and self-reported behaviors related to opioid use and harm reduction. The findings suggest that online conversations initially reflected optimism about reduced stigma and broader acceptance of harm reduction measures, but sentiment became more mixed as policy details and lived experiences surfaced. This pattern indicates that advanced LLM-based text analysis can yield deep insights into the evolving public narrative on health interventions, informing future policymaking and healthcare strategies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Organ procurement organizations (OPOs) play a crucial role in the field of organ transplantation, serving as key intermediaries in the process of organ donation. However, despite their vital function, there exists a pressing issue of transparency within the organ allocation process. This opacity not only impedes the overall effectiveness of OPOs but also raises ethical and societal concerns regarding organ distribution. This study utilizes the recently published ORCHID dataset, containing 133,101 records of organ donor referrals, to understand organ procurement and donor selection strategies in OPOs using machine learning (ML). We developed seven ML classification models to predict donor selection and the likelihood of at least four organs being suitable for transplantation, in line with established definitions of multi-organ transplantation. The models demonstrated variable recall values for donor selection, ranging between 0.62 and 0.80, while achieving consistently high performance across other evaluation metrics, notably with AUC values exceeding 0.95. Particularly in the context of multi-organ transplant predictions, the models exhibited remarkable effectiveness, with recall values spanning from 0.88 to 0.98 and AUC metrics consistently above 0.97. Administrative milestones and particular organ transplants were identified as key determinants in the organ allocation process. This study’s findings suggest significant opportunities to improve organ allocation strategies by focusing on the optimization of administrative practices, highlighting their substantial impact on transplantation success rates.

Prediabetes is a critical health condition characterized by elevated blood glucose levels that fall below the threshold for Type 2 diabetes (T2D) diagnosis. Accurate identification of prediabetes is essential to forestall the progression to T2D among at-risk individuals. This study aims to pinpoint the most effective machine learning (ML) model for prediabetes prediction and to elucidate the key biological variables critical for distinguishing individuals with prediabetes. Utilizing data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), our analysis included 6,414 participants identified as either nondiabetic or prediabetic. A rigorous selection process led to the identification of ten variables for the study, informed by literature review, data completeness, and the evaluation of collinearity. Our comparative analysis of seven ML models revealed that the Deep Neural Network (DNN), enhanced with early stop regularization, outshined others by achieving a recall rate of 60%. This model's performance underscores its potential in effectively identifying prediabetic individuals, showcasing the strategic integration of ML in healthcare. While the model reflects a significant advancement in prediabetes prediction, it also opens avenues for further research to refine prediction accuracy, possibly by integrating novel biological markers or exploring alternative modeling techniques. The results of our work represent a pivotal step forward in the early detection of prediabetes, contributing significantly to preventive healthcare measures and the broader fight against the global epidemic of Type 2 diabetes.

Depression is disproportionately prevalent among individuals with diabetes compared to the general populace, underscoring the critical need for predictive mechanisms that can facilitate timely interventions and support. This study explores the use of machine learning to forecast depression in those at risk or diagnosed with diabetes, leveraging the extensive primary care data from the Canadian Primary Care Sentinel Surveillance Network. Six machine learning models including Logistic Regression, Random Forest, AdaBoost, XGBoost, Naive Bayes, and Artificial Neural Networks were trained and evaluated on their ability to predict depression. XGBoost emerged as the most effective model with an AUC of 0.70 on the test data. Sex, age, osteoarthritis, A1c levels, and body mass index emerged as the key contributors to the best-performing model's predictive ability. While the study navigated through the constraints of limited demographic information and potential label bias, it lays a foundational premise for subsequent longitudinal studies aimed at refining depression prediction within this specific clinical cohort.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis study did not receive any funding### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data used are available online at https://cpcssn.ca/ website upon reasonable request.

Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder with increasing population incidence. However, T2DM takes years to develop, allowing onset prediction and prevention to be a clinically effective treatment strategy. In this study we propose and assess a novel approach to diabetes prediction which integrates a specialized extension of the random forest algorithm known as random survival forest (RSF). Rather than predicting a binary outcome, this machine learning model incorporates survival analysis methodology to predict the time until a patient will receive a diabetes diagnosis if their current lifestyle is maintained. We trained a baseline model on 7,704 electronic medical records from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with 14 biomarker and comorbidity features across different measurement dates. Although tuning parameters were purposefully chosen for quick training rather than for predictive performance, our model exceeded expectations with a concordance index of 0.84. Thus, RSF models have been shown to produce accurate timelines of diabetes onset trajectory, providing patients with quantifiable and relatable risks that are easy to understand. The results of our study have substantial implications for advancing machine learning in clinical decision support and patient outcome predictions, emphasizing the role of innovative models in improving predictive accuracy.

Data stand as the foundation for studying, evaluating, and addressing the multifaceted challenges within environmental health research. This chapter highlights the contributions of the Canadian Urban Environmental Health Research Consortium (CANUE) in generating and democratizing access to environmental exposure data across Canada. Through a consortium-driven approach, CANUE standardizes a variety of datasets - including air quality, greenness, neighborhood characteristics, and weather and climatic factors - into a centralized, analysis-ready, postal code-indexed database. CANUE's mandate extends beyond data integration, encompassing the design and development of environmental health-related web applications, facilitating the linkage of data to a wide range of health databases and sociodemographic data, and providing educational training and events such as webinars, summits, and workshops. The operational and technical aspects of CANUE are explored in this chapter, detailing its human resources, data sources, computational infrastructure, and data management practices. These efforts collectively enhance research capabilities and public awareness, fostering strategic collaboration and generating actionable insights that promote physical and mental health and well-being.

Rising diabetes rates have led to increased healthcare costs and health complications. An estimated half of diabetes cases remain undiagnosed. Early and accurate diagnosis is crucial to mitigate disease progression and associated risks. This study addresses the challenge of predicting diabetes prevalence in Canadian adults by employing machine learning (ML) techniques to primary care data. We leveraged the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), Canada’s premier multi-disease electronic medical record surveillance system, and developed and validated seven ML classification models to predict the likelihood of diabetes. The models were trained on clinical patient characteristics influential in predicting diabetes. We found XGBoost performed best out of all the models, with an AUC of 92%. The most important features contributing to model prediction were HbA1c level, LDL, and hypertension medication. Our research aims to aid healthcare professionals in early diagnosis and to identify key patient characteristics for targeted interventions. This study contributes to an understanding of how ML can enhance public health planning and reduce healthcare system burdens.