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Since the beginning of COVID-19, more than 13,036,550 people have been infected, and 571,574 died because of the disease by July 13, 2020. Developing new methodologies to predict the COVID-19 pandemic will help policymakers plan to contain the spread of the virus. In this research, we develop a Stochastic Fractal Search algorithm combined with a mathematical model to forecast the pandemic. To enhance the algorithm, we employed a design of the experiments approach for tuning.We applied our algorithm to public datasets to model the COVID-19 pandemic in Canada in the upcoming months. Our algorithm predicts the number of symptomatic, asymptomatic, life-threatening, recovered, and death cases. The outcomes reveal that asymptomatic cases play the main role in the transmission of the virus.Wealso show that increasing the testing capacity would enhance the detection of asymptomatic cases and limit community transmission. Moreover, we performed sensitivity analyses to discover the effects of changes in transmission rates on pandemic growth. The sensitivity analyses provide a realistic overview of the future number of cases if the transmission rates change due to the emergence of new variants or change in social measures. Considering the outcomes, we provide several managerial insights to minimize community transmission. [ABSTRACT FROM AUTHOR]

Objectives: This study modelled diabetes risk for population groups in Canada defined by socioeconomic and lifestyle characteristics and investigated inequities in diabetes risk using a validated population risk prediction algorithm. Methods: We defined population groups, informed by determinants of health frameworks. We applied the Diabetes Population Risk Tool (DPoRT) to 2017/2018 Canadian Community Health Survey data to predict 10-year diabetes risk and cases across population groups. We modelled a preventive intervention scenario to estimate reductions in diabetes for population groups and impacts on the inequity in diabetes risk across income and education. Results: The population group with at least one lifestyle and at least one socioeconomic/ structural risk factor had the highest estimated 10-year diabetes risk and number of new cases. When an intervention with a 5% relative risk reduction was modelled for this population group, diabetes risk decreased by 0.5% (females) and 0.7% (males) and the inequity in diabetes risk across income and education levels was reduced. Conclusion: Preventative interventions that address socioeconomic and structural risk factors have potential to reduce inequities in diabetes risk and overall diabetes burden. [ABSTRACT FROM AUTHOR]

Objective: To compare how different imputation methods affect the estimates and performance of a prediction model for premature mortality. Study Design and Setting: Sex-specific Weibull accelerated failure time survival models were run on four separate datasets using complete case, mode, single and multiple imputation to impute missing values. Six performance measures were compared to access predictive accuracy (Nagelkerke R2, integrated brier score), discrimination (Harrell's c-index, discrimination slope) and calibration (calibration in the large, calibration slope). Results: The highest proportion of missingness for a single variable was 10.86% for the female model and 8.24% for the male model. Comparing the performance measures for complete case, mode, single and multiple imputation: the Nagelkerke R2 values for the female model was 0.1084, 0.1116, 0.1120 and 0.111–0.1120 with the male model exhibited similar variation of 0.1050, 0.1078, 0.1078 and 0.1078–0.1081. Harrell's c-index also demonstrated small variation with values of 0.8666, 0.8719, 0.8719 and 0.8711–0.8719 for the female model and 0.8549, 0.8548, 0.8550 and 0.8550–0.8553 for the male model. Conclusion: In the scenarios examined in this study, mode imputation performed well when using a population health survey compared to single and multiple imputation when predictive performance measures is the main model goal. To generate unbiased hazard ratios, multiple imputation methods were superior. This study shows the need to consider the best imputation approach for a predictive model development given the conditions of missing data and the goals of the analysis. [ABSTRACT FROM AUTHOR]

Environmental impact assessment (EIA) has been widely criticized by the aquatic science community for poorly aligned approaches when selecting endpoints and collecting data during the baseline, predictive modeling, and postdevelopment monitoring phases. If these critical phases of the EIA process are not aligned properly, it can be difficult to evaluate the presence of postdevelopment effects. Examples of the misalignment of these phases include baseline studies failing to measure indicators that are monitored postdevelopment; predictive assessments that do not quantitatively predict conditions or potential impacts postdevelopment; and the failure to identify relevant indicators that may detect effects postdevelopment. For aquatic assessments, understanding how to protect critical ecosystem attributes to satisfy regulatory concerns could help to better align aquatic science monitoring activities across EIA phases. In this article we investigate recent Canadian hydroelectric dam EIAs to evaluate how well recent assessment approaches are meeting these necessary conditions of good aquatic EIA practice through the lens of ecosystem services from a fish's perspective. We found that larger facilities generally had baseline studies and modeling that better supported postdevelopment monitoring, but improvements in structure, linkages, and expectations would better align EIA phases in a manner that would improve assessments and environmental protection. Integr Environ Assess Manag 2024;20:616–644. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: It is critical that the phases of environmental impact assessment (EIA) be aligned so that postdevelopment effects can be detected.Baseline assessments must collect more ecosystem‐relevant data, and the predictive assessments need more quantitative modeling for ecologically relevant endpoints.There must be a stronger connection between postdevelopment monitoring and environmental management processes.More must be expected from EIAs for proper evaluation and management of the environment to occur. [ABSTRACT FROM AUTHOR]

Prediction of life expectancy in terminally ill patients is an important end-of-life care issue for patients, families and mental health workers during the last days of life. This study was conducted to examine the importance/usefulness for patients/families to have an accurate prognosis and its impact on planning their activities prior to death. All patients admitted during a period of one year were included. Patients' and families' viewpoints on the usefulness of an accurate prognosis was documented at admission. There were 285 patients in the cohort. The median time to death was 8 days. Most families (83%) rated the importance of an accurate prognosis as moderately (13%) to very much useful (70%). A total of 42% of patients were able to complete e the questionnaire. Among these, 58% found it moderately to very much useful. For families, having an accurate prognosis influenced the planning of visits (69%), communication/closure (42%) and spiritual needs/funeral arrangements (31%). Patients identified planning of visits (10%), communication/closure (12%), and goals/accomplishments (9%) as very important. Discussing the prognosis and its impact is very helpful for the mental health professionals to have open and honest conversations with patients/families to identify, prioritize and adapt treatment to achieve goals prior to death. [ABSTRACT FROM AUTHOR]

Purpose: COVID-19 impact on the population's mental health has been reported worldwide. Predicting healthcare workers' mental health and life stress is needed to proactively plan for future emergencies. Design: Statistics Canada has surveyed Canadian healthcare workers and those working in healthcare settings to gauge their perceived mental health and perceived life stress. Setting: A cross-sectional survey of healthcare workers in Canada. Subjects: A sample of 18,139 healthcare workers respondents. Analysis: Eight algorithms, including Logistic Regression, Random Forest (RF), Naive Bayes (NB), K Nearest Neighbours (KNN), Adaptive boost (AdaBoost), Multi-layer perceptron (MLP), XGBoost, and LightBoost. AUC scores, accuracy and precision were measured for all models. Results: XGBoost provided the highest performing model AUC score (AUC = 82.07%) for predicting perceived mental health, and Random Forest performed the best for predicting perceived life stress (AUC = 77.74%). Perceived health, age group of participants, and perceived mental health compared to before the pandemic were found to be the most important 3 features to predict perceived mental health and perceived stress. Perceived mental health compared to before the pandemic was the most important predictor for perceived life stress. Conclusion: Our models are highly predictive of healthcare workers' perceived mental health and life stress. Implementing scalable, non-expensive virtual mental health solutions to address mental health challenges in the workplace could mitigate the impact of workplace conditions on healthcare workers' mental health. [ABSTRACT FROM AUTHOR]

Purpose: Coronary artery disease (CAD) patients frequently face readmissions due to suboptimal disease management. Prediction models are pivotal for detecting early unplanned readmissions. This review offers a unified assessment, aiming to lay the groundwork for enhancing prediction models and informing prevention strategies. Methods: A search through five databases (PubMed, Web of Science, EBSCOhost, Embase, China National Knowledge Infrastructure) up to September 2023 identified studies on prediction models for coronary artery disease patient readmissions for this review. Two independent reviewers used the CHARMS checklist for data extraction and the PROBAST tool for bias assessment. Results: From 12,457 records, 15 studies were selected, contributing 30 models targeting various CAD patient groups (AMI, CABG, ACS) from primarily China, the USA, and Canada. Models utilized varied methods such as logistic regression and machine learning, with performance predominantly measured by the c-index. Key predictors included age, gender, and hospital stay duration. Readmission rates in the studies varied from 4.8% to 45.1%. Despite high bias risk across models, several showed notable accuracy and calibration. Conclusion: The study highlights the need for thorough external validation and the use of the PROBAST tool to reduce bias in models predicting readmission for CAD patients. [ABSTRACT FROM AUTHOR]

The article discusses the development and validation of PROGRxN-BCa, an artificial intelligence-based tool for predicting 5-year progression risk in non-muscle-invasive bladder cancer (NMIBC). It outperforms current prognostic tools, improves stratification of intermediate-risk patients, and demonstrates high accuracy in predicting time to progression, offering potential benefits for treatment decision-making and clinical trial enrollment.

Brand communities are of great interest to marketers, and motivating participation in these communities has been explored from many different perspectives. The current study examined how men and women differ in the motives (i.e., entertainment, receiving and sharing information, social integration, self-discovery, and status enhancement) behind their behavior rather than the more often discussed difference between men and women regarding actual behavior and attitudes. A survey sample (N = 195) was collected to explore this, and the data were analyzed with PLS-SEM. The model of participation motives for women was compared to the model for men, and differences (and similarities) were analyzed. The findings indicate that the genders have different motivations to participate in these brand communities. Specifically, the self-discovery and sharing information motives were significantly more important for males, while the social integration motive was significantly more important for females. However, the genders had no differences regarding entertainment, receiving information, and status enhancement motives. [ABSTRACT FROM AUTHOR]

Construction, by the nature of the work, is more accident-prone than other industries despite advancements in improving safety performance. Proactive mitigation and assessment of the safety performance of construction projects remain challenging due to the difficulty of acquiring, storing, and using data to produce accurate predictive models. This research focused on devising methods that allow decision makers to leverage existing data in the planning phase to streamline the development of predictive models. A data-driven approach to predict the probability of a safety incident occurring in a given construction project and within a novel discipline-level schedule is presented. By implementing the proposed model, decision makers can evaluate and mitigate the risk of a given project incident occurring by deploying discipline-level safety policies in the planning phase and modifying the schedule accordingly. A predictive model was developed based on selected safety-related metrics extracted from a data set comprising daily payroll data and incident reports, which represent 28 million working hours within eight different industrial construction projects in Canada. The model was implemented in a case study based on an industrial project to demonstrate the framework's functionality and practical utility during the project planning phase. The results show that the revised safe plan can be achieved by incorporating safety considerations in the planning phase. This research provides a practical solution for enhancing safety in the planning phase of construction projects using a data-driven model. By leveraging existing historical data, decision makers can predict potential safety incidents within specific disciplines without the need for detailed quantitative planning information. This approach also enables effective adjustments to be made to the schedule in order to mitigate risks. Furthermore, the discipline-level approach facilitates proactive safety planning by implementing discipline-specific safety policies that align with the unique characteristics of each discipline. Using a case study based on an industrial project, the proposed framework demonstrated its functionality and practical utility by identifying suitable safety-related metrics that construction enterprises typically record. These sources can include safety-related data, such as incident reports, as well as data recorded for other purposes, such as payroll data. The results highlight that incorporating safety considerations in the planning phase enables the development of a revised safety plan. In conclusion, by considering safety-related metrics and utilizing human resources (HR) data available in all companies, organizations can proactively assess and improve safety performance. [ABSTRACT FROM AUTHOR]

Background: To develop and validate a prediction tool, or nomogram, for the risk of a decline in cognitive performance based on the interRAI Cognitive Performance Scale (CPS). Methods: Retrospective, population-based, cohort study using Canadian Resident Assessment Instrument for Home Care (RAI-HC) data, collected between 2010 and 2018. Eligible home care clients, aged 18+, with at least two assessments were selected randomly for model derivation (75%) and validation (25%). All clients had a CPS score of zero (intact) or one (borderline intact) on intake into the home care program, out of a possible score of six. All individuals had to remain as home care recipients for the six months observation window in order to be included in the analysis. The primary outcome was any degree of worsening (i.e., increase) on the CPS score within six months. Using the derivation cohort, we developed a multivariable logistic regression model to predict the risk of a deterioration in the CPS score. Model performance was assessed on the validation cohort using discrimination and calibration plots. Results: We identified 39,292 eligible home care clients, with a median age of 79.0 years, 62.3% were female, 38.8% were married and 38.6% lived alone. On average, 30.3% experienced a worsening on the CPS score within the six-month window (i.e., a change from 0 or 1 to 2, 3, 4, 5, or 6). The final model had good discrimination (c-statistic of 0.65), with excellent calibration. Conclusions: The model accurately predicted the risk of deterioration on the CPS score over six months among home care clients. This type of predictive model may provide useful information to support decisions for home care clinicians who use interRAI data internationally. [ABSTRACT FROM AUTHOR]

Objective: Healthcare is increasingly digitized, yet remote and automated machine learning (ML) triage prediction systems for virtual urgent care use remain limited. The Canadian Triage and Acuity Scale (CTAS) is the gold standard triage tool for in-person care in Canada. The current work describes the development of a ML-based acuity score modelled after the CTAS system. Methods: The ML-based acuity score model was developed using 2,460,109 de-identified patient-level encounter records from three large healthcare organizations (Ontario, Canada). Data included presenting complaint, clinical modifiers, age, sex, and self-reported pain. 2,041,987 records were high acuity (CTAS 1–3) and 416,870 records were low acuity (CTAS 4–5). Five models were trained: decision tree, k-nearest neighbors, random forest, gradient boosting regressor, and neural net. The outcome variable of interest was the acuity score predicted by the ML system compared to the CTAS score assigned by the triage nurse. Results: Gradient boosting regressor demonstrated the greatest prediction accuracy. This final model was tuned toward up triaging to minimize patient risk if adopted into the clinical context. The algorithm predicted the same score in 47.4% of cases, and the same or more acute score in 95.0% of cases. Conclusions: The ML algorithm shows reasonable predictive accuracy and high predictive safety and was developed using the largest dataset of its kind to date. Future work will involve conducting a pilot study to validate and prospectively assess reliability of the ML algorithm to assign acuity scores remotely. [ABSTRACT FROM AUTHOR]

To identify profiles of aging by combining psychological distress, cognition and functional disability, and their associated factors. Data were drawn from the Étude sur la Santé des Aînés-Services study and included 1585 older adults. Sociodemographic, psychosocial, lifestyle and health factors were informed from structured interviews. Group-based multi-trajectory modeling and multinomial logistic regression were used to identify aging profiles and correlates. Sampling weights were applied to account for the sampling plan. The weighted sample size was 1591. Three trajectories were identified: a favorable (79.0%), intermediate (14.5%), and severe scenario (6.5%). Factors associated with the severe scenario were older age, male gender, lower education, the presence of anxiety disorders, low physical activity, and smoking. Membership in the intermediate scenario was associated with daily hassles, physical disorders, anxiety and depression, antidepressant/psychotherapy use, low physical activity, and no alcohol use. High social support was protective against less favorable profiles. Symptoms of anxiety and depression and high burden of physical disorders were associated with less favorable trajectories. Modifiable lifestyle factors have a significant effect on healthy aging. Assessment and management of anxio-depressive symptoms are important in older adults. Clinical interventions including access to psychotherapy and promotion of healthier lifestyles should be considered. [ABSTRACT FROM AUTHOR]

Objectives Damage accrual in SSc can be tracked using the Scleroderma Clinical Trials Consortium Damage Index (DI). Our goal was to develop a prediction model for damage accrual in SSc patients with early disease. Methods Using patients with <2 years disease duration from Canada and Australia as a derivation cohort, and from the Netherlands as a validation cohort, we used group-based trajectory modelling (GBTM) to determine 'good' and 'bad' latent damage trajectories. We developed a prediction model from this analysis and applied it to patients from derivation and validation cohorts. We plotted the actual DI trajectories of the patients predicted to be in 'good' or 'bad' groups. Results We found that the actual trajectories of damage accumulation for lcSSc and dcSSc were very different, so we studied each subset separately. GBTM found two distinct trajectories in lcSSc and three in dcSSc. We collapsed the two worse trajectories in the dcSSc into one group and developed a prediction model for inclusion in either 'good' or 'bad' trajectories. The performance of models using only baseline DI and sex was excellent with ROC AUC of 0.9313 for lcSSc and 0.9027 for dcSSc. Using this model, we determined whether patients would fall into 'good' or 'bad' trajectory groups and then plotted their actual trajectories which showed clear differences between the predicted 'good' and 'bad' cases in both derivation and validation cohorts. Conclusions A simple model using only cutaneous subset, baseline DI and sex can predict damage accumulation in early SSc. [ABSTRACT FROM AUTHOR]

Aims: Male driving while impaired (DWI) offenders are at heightened risk for engaging in risky driving. Males in a depressed mood are also more prone to alcohol misuse, which may further contribute to risky driving. This manuscript investigates the predictive potential of combined depressed mood and alcohol misuse on risky driving outcomes 3 and 9 years after baseline in male DWI offenders. Methods: At baseline, participants completed questionnaires assessing depressed mood (Major Depression scale of the Millon Clinical Multiaxial Inventory-III), alcohol misuse (Alcohol Use Disorders Identification Test), and sensation-seeking (Sensation Seeking Scale-V). Risky driving data (Analyse des comportements routiers; ACR3) were collected at follow-up 3 years after baseline. Driving offence data were obtained for 9 years after baseline. Results: There were 129 participants. As 50.4% of the sample were missing ACR3 scores, multiple imputation was conducted. In the final regression model, R2  = 0.34, F (7,121) = 8.76, P  < 0.001, alcohol misuse significantly predicted ACR3, B  = 0.56, t  = 1.96, P  = 0.05. Depressed mood, however, did not significantly predict ACR3 and sensation-seeking was not a significant moderator. Although the regression model predicting risky driving offences at Year 9 was significant R2  = 0.37, F (10,108) = 6.41, P  < 0.001, neither depressed mood nor alcohol misuse was a significant predictor. Conclusions: These findings identify alcohol misuse as a predictor of risky driving 3 years after baseline among male DWI offenders. This enhances our prediction of risky driving, extending beyond the widely researched acute impacts of alcohol by exploring chronic patterns. [ABSTRACT FROM AUTHOR]

Purpose: Socially disadvantaged patients may lack self-efficacy to navigate a complex health system making them vulnerable to healthcare inequity. We aimed to develop an Index of social vulnerability that predicts increased risk of negative healthcare events (e.g. emergency hospitalization), independent of chronic disease burden. The analysis illustrates the conceptual and practical steps leading to the development of a pragmatic Index of social vulnerability to limited healthcare self-efficacy. Methods: Using data from a 3-year cohort of 2507 adult primary care patients in Québec (Canada), we applied two complementary structural equation modelling approaches—Partial Least Squares Path Modelling (PLS-PM) and Multiple indicators and Multiple Causes (MIMIC) modelling—to identify a minimal set of social characteristics that could be summed into an Index related to limited healthcare self-efficacy. We then used logistic regression to determine if the Index predicted: hospital emergency department use; hospital admissions; unmet need for care, and others. We privileged parsimony over explanatory capacity in our analytic decisions to make the Index pragmatic for epidemiologic and clinical use. Results: The Individual Social Vulnerability Index is the weighted sum of five indicators: two indicators of social support; educational achievement; financial status; limited language proficiency. The Index predicts increased likelihood of all negative healthcare outcomes except unmet need, with a clear threshold at Index ≥ 2. The effect is independent of chronic disease burden. Conclusion: When social deficits outweigh social assets by two or more (Index ≥ 2), there is an increased risk of negative healthcare events beyond the risk attributable to poor health. The Index is a pragmatic tool to identify a minority of patients who will require additional support to receive equitable healthcare. [ABSTRACT FROM AUTHOR]

High performance of HVAC connected Building Integrated Photovoltaic/Thermal (BIPV/T) systems relies on appropriate control. However, optimal control is often overlooked, resulting in systems that operate inefficiently. This paper investigates how model predictive control (MPC) can improve the operation of open loop air-based BIPV/T systems connected to multiple thermal applications. The BIPV/T system at the first institutional net-zero energy building in Canada, the Varennes library, is used as an archetype. The BIPV/T covers 16% of the south-facing roof and operates under a simple rule-based control strategy. The developed control and design strategies consider variations of this system, to achieve higher thermal utilization efficiency. A control-oriented BIPV/T model is developed and calibrated using monitored data. The BIPV/T airflow is regulated through MPC for simultaneous heat supply to an Energy Recovery Ventilator and air-to-water heat pump. The BIPV/T air flow is efficiently controlled, considering the connected thermal applications, environmental conditions, and PV temperature. Model-based control for BIPV/T systems can increase the amount of useful heat and reduce PV overheating. The MPC controller for the examined system reduced the building energy consumption compared to the business-as-usual operation by 40% and together with increased BIPV/T area can supply excess heat to adjacent buildings. [ABSTRACT FROM AUTHOR]

Rationale: Limited information exists about the epidemiology, outcomes, and predictors of weaning from mechanical ventilation in patients with spinal cord injury. Objectives: Our aim was to investigate predictors of weaning outcomes for patients with traumatic spinal cord injury (tSCI) and develop and validate a prognostic model and score for weaning success. Methods: This was a registry-based, multicentric cohort study including all adult patients with tSCI requiring mechanical ventilation (MV) and admitted to one of the intensive care units (ICUs) of the Trauma Registry at St. Michael’s Hospital (Toronto, ON, Canada) and the Canadian Rick Hansen Spinal Cord Injury Registry between 2005 and 2019. The primary outcome was weaning success from MV at ICU discharge. Secondary outcomes included weaning success at Days 14 and 28, time to liberation from MV accounting for competing risk of death, and ventilator-free days at 28 and 60 days. Associations between baseline characteristics and weaning success or time to liberation from MV were measured using multivariable logistic and competing risk regressions. A parsimonious model to predict weaning success and ICU discharge was developed and validated via bootstrap. A prediction score for weaning success at ICU discharge was derived, and its discriminative ability was assessed using receiver operating characteristic curve analysis and compared with the Injury Severity Score (ISS). Results: Of 459 patients analyzed, 246 (53.6%), 302 (65.8%), and 331 (72.1%) were alive and free of MV at Day 14, Day 28, and ICU discharge, respectively; 54 (11.8%) died in the ICU. Median time to liberation from MV was 12 days. Factors associated with weaning success were Blunt injury (odds ratio [OR], 2.96; P = 0.010), ISS (OR, 0.98; P = 0.025), Complete syndrome (OR, 0.53; P = 0.009), age in Years (OR, 0.98; P = 0.003), and Cervical LEsion (OR, 0.60; P = 0.045). The BICYCLE score showed a greater area under the curve than the ISS (0.689 [95% confidence interval (CI), 0.631–0.743] vs. 0.537 [95% CI, 0.479–0.595]; P, 0.0001). Factors predicting weaning success also predicted time to liberation. Conclusions: In a large multicentric cohort, 72% of patients with tSCI were weaned and discharged alive from the ICU. Readily available admission characteristics can reasonably predict weaning success and help prognostication. [ABSTRACT FROM AUTHOR]

Neurodevelopmental disability in children covers a vast array of congenital and acquired long-term conditions associated with brain or neuromuscular impairments that impact function. While some presentations of neurodevelopmental disability align with diagnostic labels, many do not, leaving children whose conditions don't fit neatly under diagnostic labels struggling to access services or families and professionals feeling pressured to assign a diagnostic label in order to access services. In this paper, we (1) discuss the evidence showing that there is often a mismatch between a child's neurodevelopmental diagnosis, or lack of diagnosis, and function, (2) comment on the inequities exacerbated by diagnosis-based approaches for services, and (3) highlight the potential benefits of using a function and participation-based approach for providing services to children with neurodevelopmental disabilities. We close with three calls to action for function and participation-based approaches that could better support equitable services for children with neurodevelopmental disabilities. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosing most cancers at an earlier stage improves outcomes because treatments are more effective and often less invasive. This study looked at the health patterns of adults enrolled in Alberta's Tomorrow Project before they were diagnosed with cancer to identify factors related to cancers that are caught early or late. These identified factors were then tested in a similar group of adults from the British Columbia Generations Project who also subsequently developed cancer, to see how well they could predict stage at diagnosis. The confirmed health patterns can be used to improve cancer prevention programs and activities to find cancer earlier in people who are at most risk of being diagnosed with late-stage cancer. Risk prediction models for cancer stage at diagnosis may identify individuals at higher risk of late-stage cancer diagnoses. Partial proportional odds risk prediction models for cancer stage at diagnosis for males and females were developed using data from Alberta's Tomorrow Project (ATP). Prediction models were validated on the British Columbia Generations Project (BCGP) cohort using discrimination and calibration measures. Among ATP males, older age at diagnosis was associated with an earlier stage at diagnosis, while full- or part-time employment, prostate-specific antigen testing, and former/current smoking were associated with a later stage at diagnosis. Among ATP females, mammogram and sigmoidoscopy or colonoscopy were associated with an earlier stage at diagnosis, while older age at diagnosis, number of pregnancies, and hysterectomy were associated with a later stage at diagnosis. On external validation, discrimination results were poor for both males and females while calibration results indicated that the models did not over- or under-fit to derivation data or over- or under-predict risk. Multiple factors associated with cancer stage at diagnosis were identified among ATP participants. While the prediction model calibration was acceptable, discrimination was poor when applied to BCGP data. Updating our models with additional predictors may help improve predictive performance. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) landfills using leachate recirculation optimize the waste stabilization process by providing nutrition and moisture for biological activity. However, the leachate recirculation creates an environment with complex processes due to accelerated biodegradation and generated heat. Additionally, different timing in waste placement and the heterogeneous nature of MSW cause significant variations in properties throughout the intercalated layers. In this study, a settlement framework model employing Thermal–Hydraulic–Mechanical–Biochemical processes is proposed, which considers multiple MSW properties including temperature, pH, and saturation. The framework model includes a modified Cam-clay model to simulate short-term settlement and adopts mechanical and biological creep models for long-term settlement estimation. A long-term biological creep model that uses a single decay rate constant is revised to account for environmental factors such as temperature, pH, and saturation in estimating MSW decay rates. The framework model was calibrated using the data of large-scale column experiments, which were conducted at different temperatures and saturation conditions considering biodegradation rates. Also, an MSW placement strategy was developed to consider the effect of different waste layer placement timing in the progression of MSW landfill total settlement. The modeling framework was validated using settlement data from a landfill in Canada. The results showed that temperature and saturation have a significant influence on MSW settlement and therefore should be considered in MSW landfill settlement prediction models. In municipal solid waste (MSW) landfills, leachate recirculation increases biological activity and heat and therefore results in further breakdown of waste material and, subsequently, large settlements. Also, different climates and waste placement seasons, in addition to the nonhomogenous nature of MSW, create variations in landfill layers that need to be addressed. This study investigated a settlement model that was verified with an experiment where different temperature and saturation was applied to MSW. The settlement was divided into short-term settlement (from the compression of material) and long-term settlement from the breakdown of material with time. Additionally, since the whole MSW is not placed in a landfill at once, a strategy was developed to consider the different stages of waste placement in the developed model. The developed framework was verified using the data collected from a landfill in Canada. The results show that when we consider temperature and the amount of saturation in MSW, we can have a better prediction of MSW settlement and prevent unwanted consequences of landfill failure. [ABSTRACT FROM AUTHOR]

The enactment of COVID-19 policies in Canada falls under provincial jurisdiction. This study exploits time-series variation across four Canadian provinces to evaluate the effects of stricter COVID-19 policies on daily case counts. Employing data from this time-period allows an evaluation of the efficacy of policies independent of vaccine impacts. While both OLS and IV results offer evidence that more stringent Non-Pharmaceutical Interventions (NPIs) can reduce daily case counts within a short time-period, IV estimates are larger in magnitude. Hence, studies that fail to control for simultaneity bias might produce confounded estimates of the efficacy of NPIs. However, IV estimates should be treated as correlations given the possibility of other unobserved determinants of COVID-19 spread and mismeasurement of daily cases. With respect to specific policies, mandatory mask usage in indoor spaces and restrictions on business operations are significantly associated with lower daily cases. We also test the efficacy of different forecasting models. Our results suggest that Gradient Boosted Regression Trees (GBRT) and Seasonal Autoregressive-Integrated Moving Average (SARIMA) models produce more accurate short-run forecasts relative to Vector Auto Regressive (VAR), and Susceptible–Infected–Removed (SIR) epidemiology models. Forecasts from SIR models are also inferior to results from basic OLS regressions. However, predictions from models that are unable to correct for endogeneity bias should be treated with caution. [ABSTRACT FROM AUTHOR]

Background: In infertility treatment, blastocyst morphological grading is commonly used in clinical practice for blastocyst evaluation and selection, but has shown limited predictive power on live birth outcomes of blastocysts. To improve live birth prediction, a number of artificial intelligence (AI) models have been established. Most existing AI models for blastocyst evaluation only used images for live birth prediction, and the area under the receiver operating characteristic (ROC) curve (AUC) achieved by these models has plateaued at ~0.65. Methods: This study proposed a multimodal blastocyst evaluation method using both blastocyst images and patient couple's clinical features (e.g., maternal age, hormone profiles, endometrium thickness, and semen quality) to predict live birth outcomes of human blastocysts. To utilize the multimodal data, we developed a new AI model consisting of a convolutional neural network (CNN) to process blastocyst images and a multilayer perceptron to process patient couple's clinical features. The data set used in this study consists of 17,580 blastocysts with known live birth outcomes, blastocyst images, and patient couple's clinical features. Results: This study achieved an AUC of 0.77 for live birth prediction, which significantly outperforms related works in the literature. Sixteen out of 103 clinical features were identified to be predictors of live birth outcomes and helped improve live birth prediction. Among these features, maternal age, the day of blastocyst transfer, antral follicle count, retrieved oocyte number, and endometrium thickness measured before transfer are the top five features contributing to live birth prediction. Heatmaps showed that the CNN in the AI model mainly focuses on image regions of inner cell mass and trophectoderm (TE) for live birth prediction, and the contribution of TE-related features was greater in the CNN trained with the inclusion of patient couple's clinical features compared with the CNN trained with blastocyst images alone. Conclusions: The results suggest that the inclusion of patient couple's clinical features along with blastocyst images increases live birth prediction accuracy. Funding: Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Program. [ABSTRACT FROM AUTHOR]

The airtightness of buildings has a significant impact on buildings' energy efficiency, maintenance and occupant comfort. The main goal of this study is to provide an evaluation of the air leakage characteristics of dwellings in different regions in Canada. This study evaluated the key influencing factors on airtightness performance based on a large set of measured data (73,450 dwellings located in Canada with 11 measurement parameters for each). Machine learning models based on multivariate regression (MVR) and Random Forest Ensemble (RFE) were developed to predict the air leakage value. The RFE model, which shows better results than MVR, was used to evaluate the effect of the ageing of buildings. Results showed that the maximum increase in air leakage occurs during the first year after construction – approximately 25%, and then 3.7% in the second year, after which the increase rate becomes insignificant and relatively constant – approximately 0.3% per year. The findings from this study can provide significant information for building designs, building performance simulations and strengthening standards and guidelines policies on indoor environmental quality. [ABSTRACT FROM AUTHOR]

The volcanic-hosted massive sulfide (VHMS) deposits of the Bathurst Mining Camp have been significant contributors to Canada's historic Zn, Pb, Cu, and Ag production. Most of these deposits are hosted by the Tetagouche Group, many of which occur at the top of the Nepisiguit Falls Formation (footwall) along the contact with the conformably overlying Flat Landing Brook Formation (hanging wall), i.e., the Brunswick belt. Exploration along this prolific belt can benefit greatly from predictive modeling of prospectivity for VHMS deposits using machine learning (ML)-based mineral prospectivity mapping (MPM), which is the chief objective of this study. However, ML-aided MPM of this belt is faced with three challenges: (i) weak geochemical and geophysical signatures of VHMS deposits (i.e., poor predictor variables) owing to the complex tectono-stratigraphy of the host sequence and extensive glacial cover, (ii) over-fitting stemming from its limited number of VHMS deposits, and (iii) stochastic uncertainties of predictive models linked to the diversity in local geological settings of various VHMS deposits. This study adopted an ensemble regularized regression methodology combining ensemble modeling with LASSO, Ridge, and Elastic Net regularized regression techniques for addressing the above challenges. Herein, we demonstrate that the adopted framework can reduce the severity of over-fitting, handle poor predictor variables, and mitigate the effects of stochastic uncertainties in ML-based MPM. These results are followed by discussions of the pros and cons of the framework adopted in this study. [ABSTRACT FROM AUTHOR]

Approximately 3.5% of deliveries in Canada result in potentially preventable neonatal readmission, often times due to preventable morbidities. With complexities in hospital discharge planning, health care providers may benefit in identifying infants at risk of readmission for additional monitoring. To develop and validate models for predicting 7-day neonatal readmission following vaginal or cesarean births. All liveborn term singleton infants without congenital anomalies in the province of Alberta who were not admitted to the NICU were identified using perinatal and hospitalization databases. A temporal split-sample was used for model development (2012–2014, vaginal n = 63,378; cesarean n = 21,225) and external validation (2014–2015, vaginal n = 21,583, cesarean n = 7,477). Multivariable logistic regression models using backward stepwise selection were used to identify predictors of 7-day readmission. We evaluated predictors of maternal age, Apgar score, length-of-stay, birthweight, gestational age, parity, residence, and sex. Hosmer-Lemeshow test and c-statistics were used to estimate calibration and discrimination. The rate of readmission was 3.3% (95% CI 3.1%, 3.4%) and 2.1% (95% CI 1.9%, 2.3%) following vaginal and cesarean births in the development dataset. Prediction model following vaginal birth, excluding predictors of length-of-stay and birthweight, had sub-optimal performance in development (c-statistics 0.69) and validation data (c-statistics 0.68). Prediction model following cesarean birth, excluding predictors of maternal age, birthweight, and residence, had sub-optimal performance in development (c-statistics 0.62) and validation data (c-statistics 0.64). Readmission was observed in 7.9% (95% CI 7.1%, 8.8%) and 4.9% (95% CI 3.9%, 6.1%) of infants of vaginal and cesarean births, respectively, in the top quintile for the risk of 7-day readmission. Using routinely collected administrative data, we developed and validated prediction models for neonatal readmission following vaginal and cesarean births. Presently the model is sub-optimal for use in risk assessment and planning at discharge, however, additional information may improve the predictive performance. [ABSTRACT FROM AUTHOR]

Simple Summary: Increasing knowledge of outcome prognostication, derived from various clinical and imaging-derived parameters, has the potential for pretreatment risk stratification, allowing clinicians to deliver more patient specific treatment tailored to individual risk. Our study indicates that a prognostic model, combining clinical parameter, PET and CT radiomics with sarcopenic status, derived from standard of care clinical 18F-FDG-PET/CT improves outcome prognostication among patients with advanced, metastatic esophageal and gastroesophageal cancer. We investigated, whether 18[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29–91 years) undergoing 18[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients' nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid 18[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer. [ABSTRACT FROM AUTHOR]

Purpose Screening for lung cancer is recommended to reduce lung cancer mortality, but there is no consensus on patient selection for screening in Canada. Risk prediction models are more efficacious than the screening recommendations of the Canadian Task Force on Preventive Health Care (CTFPHC), but it remains to be determined which model and threshold are optimal. Methods We retrospectively applied the PLCOm2012, PLCOall2014 and LLPv2 risk prediction models to 120 lung cancer patients from a Canadian province, at risk thresholds of ≥ 1.51% and ≥ 2.00%, to determine screening eligibility at time of diagnosis. OncoSim modelling was used to compare these risk thresholds. Results Sensitivities of the risk prediction models at a threshold of ≥ 1.51% were similar with 93 (77.5%), 96 (80.0%), and 97 (80.8%) patients selected for screening, respectively. The PLCOm2012 and PLCOall2014 models selected significantly more patients for screening at a ≥ 1.51% threshold. The OncoSim simulation model estimated that the ≥ 1.51% threshold would detect 4 more cancers per 100 000 people than the ≥ 2.00% threshold. All risk prediction models, at both thresholds, achieved greater sensitivity than CTFPHC recommendations, which selected 56 (46.7%) patients for screening. Conclusion Commonly considered lung cancer screening risk thresholds (≥1.51% and ≥2.00%) are more sensitive than the CTFPHC 30-pack–years criterion to detect lung cancer. A lower risk threshold would achieve a larger population impact of lung cancer screening but would require more resources. Patients with limited or no smoking history, young patients, and patients with no history of COPD may be missed regardless of the model chosen. [ABSTRACT FROM AUTHOR]

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues. [ABSTRACT FROM AUTHOR]

Governments have implemented different interventions and response models to combat the spread of COVID-19. The necessary intensity and frequency of control measures require us to project the number of infected cases. Three short-term forecasting models were proposed to predict the total number of infected cases in Canada for a number of days ahead. The proposed models were evaluated on how their performance degrades with increased forecast horizon, and improves with increased historical data by which to estimate them. For the data analyzed, our results show that 7 to 10 weeks of historical data points are enough to produce good fits for a two-weeks predictive model of infected case numbers with a NRMSE of 1% to 2%. The preferred model is an important quick-deployment tool to support data-informed short-term pandemic related decision-making at all levels of governance. [ABSTRACT FROM AUTHOR]

Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients' sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups. [ABSTRACT FROM AUTHOR]

The present study first describes the variations in concentrations of 12 chemical elements in groundwater relative to salinity levels in Southern Quebec (Canada) groundwater systems, and then uses this data to develop an empirical predictive model for evaluating groundwater chemical composition relative to salinity levels. Data is drawn from a large groundwater chemistry database containing 2608 samples. Eight salinity classes were established from lowest to highest chloride (Cl) concentrations. Graphical analyses were applied to describe variations in major, minor, and trace element concentrations relative to salinity levels. Results show that the major elements were found to be dominant in the lower salinity classes, whereas Cl becomes dominant at the highest salinity classes. For each of the major elements, a transitional state was identified between domination of the major elements and domination of Cl. This transition occurred at a different level of salinity for each of the major elements. Except for Si, the minor elements Ba, B, and Sr generally increase relative to the increase of Cl. The highest Mn concentrations were found to be associated with only the highest levels of Cl, whereas F was observed to be more abundant than Mn. Based on this analysis of the data, a correlation table was established between salinity level and concentrations of the chemical constituents. We thus propose a predictive empirical model, identifying a profile of the chemical composition of groundwater relative to salinity levels, to help homeowners and groundwater managers evaluate groundwater quality before resorting to laborious and costly laboratory analyses. [ABSTRACT FROM AUTHOR]

Calves born on Eastern Canadian dairy farms that are not kept in the herds are traditionally sold through auction markets and are raised for meat purposes such as veal calves. Since February 2020, a new Canadian federal regulation has forbidden calves <9 d old to be sold through auction markets. However, in the absence of a real-time birth registry consultation system, it would be of interest to look for predictors that could be associated with age to allow identification of calves too young to be transported. In the current retrospective cross-sectional study, 1,178 calves with a declared birth date (411 calves aged <9 d old; 34.9%) were assessed in 2 large Québec auction sites. Easy-to-record covariates [body weight (BW), breed phenotype, and presence of an umbilical cord remnant] as well as other clinical signs (umbilical swelling, enlargement, umbilical pain, wet umbilicus, skin tent, sunken eyes, ocular and nasal secretion, and hide cleanliness) were assessed. Two logistic regression models using age as a dichotomous dependent variable (<9 d old vs ≥9 d old) were built. The first model (model 1) considered all covariates, which were selected after univariable analyses and a backward stepwise selection process, whereas a more pragmatic model (model 2) only included the 3 easy-to-record variables (i.e., BW, breed, umbilical cord). Both models had similar accuracy to detect calves <9 d old (sensitivity of 38.4 and 37.5%, and specificity of 85.7 and 84.6% for model 1 and 2, respectively). Model 2 was subsequently more specifically studied as it employs a faster and easier assessment. Decision thresholds were tested for their robustness based on misclassification cost term (MCT) analysis with various prevalence of calves <9 d old and various costs of false-negative:false-positive ratio. Despite statistical significance, model accuracy, even if refined with MCT analysis, was limited at the individual level, showing the limits of using physical signs and BW or their combination as a reliable proxy of age. The sensitivity of these models to find calves <9 d old was not to be used for monitoring compliance with the Canadian federal regulation. The relatively high model specificity may help to use this model as a rule-in test (i.e., targeting positive calves for further investigation) rather than a rule-out test (due to its low sensitivity). [ABSTRACT FROM AUTHOR]

Predicting and modeling time-to-events data is a crucial and interesting research area. For modeling and predicting such types of data, numerous statistical models have been suggested and implemented. This study introduces a new statistical model, namely, a new modified flexible Weibull extension (NMFWE) distribution for modeling the mortality rate of COVID-19 patients. The introduced model is obtained by modifying the flexible Weibull extension model. The maximum likelihood estimators of the NMFWE model are obtained. The evaluation of the estimators of the NMFWE model is assessed in a simulation study. The flexibility and applicability of the NMFWE model are established by taking two datasets representing the mortality rates of COVID-19-infected persons in Mexico and Canada. For predictive modeling, we consider two pure statistical models and two machine learning (ML) algorithms. The pure statistical models include the autoregressive moving average (ARMA) and non-parametric autoregressive moving average (NP-ARMA), and the ML algorithms include neural network autoregression (NNAR) and support vector regression (SVR). To evaluate their forecasting performance, three standard measures of accuracy, namely, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are calculated. The findings demonstrate that ML algorithms are very effective at predicting the mortality rate data. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to explore caregiver and family quality of life in families with children with autism spectrum disorder (ASD). To better understand the experience of caregivers and families of children with ASD, a family systems perspective using constructs from a psychosocial adjustment model of disability (i.e., role impact as the independent variable and relationship satisfaction and mastery as mediator variables) was studied to explain caregiver and family quality of life. Method: Hayes' PROCESS macro was used to determine the variance of caregiver and family quality of life accounted for by the predictor variables. Results: The results indicated that the models explained 52.3% of the variance in caregiver quality of life and 44.4% of the variance in family quality of life. Conclusions: The findings of this study expand the current literature on caregivers of children with ASD, and the important variables to consider related to caregiver quality of life and family quality of life. Implications for rehabilitation research (e.g., increased need for a focus on family variables across research agendas) and practice (e.g., increase need for family-oriented interventions) are discussed. [ABSTRACT FROM AUTHOR]

Hospitals in Canada are facing a crisis-level shortage of critical supplies and equipment during the COVID-19 pandemic. This motivates us to create predictive models that can use Canada COVID-19 data and pandemic-related factors to accurately forecast 5 quantities—three related to hospital resource utilization (i.e., the number of hospital beds, ICU beds, and ventilators that will be needed by COVID-19 patients) and two to the pandemic progress (i.e., the number of COVID-19 cases and COVID-19 deaths)—several weeks in advance. We developed a machine learning method that can use information (i.e., resource utilization, pandemic progress, population mobility, weather condition, and public policy) currently known about a region since March 2020, to learn multiple temporal convolutional network (TCN) models every week; each used for forecasting the weekly average of one of these 5 quantities in Canada (respectively, in six specific provinces) for each, in the next 1 (resp., 2,3,4) weeks. To validate the effectiveness of our method, we compared our method, versus other standard models, on the COVID-19 data and hospital resource data, on the tasks of predicting the 116 values (for Canada and its six most populated provinces), every week from Oct 2020 to July 2021, and the 20 values (only for Canada) for four specific times within 9 July to 31 Dec 2021. Experimental results show that our 4640 TCN models (each forecasting a regional target for a specific future time, on a specific date) can produce accurate 1,2,3,4-week forecasts of the utilization of every hospital resource and pandemic progress for each week from 2 Oct 2020 to 2 July 2021, as well as 80 TCN models for each of the four specified times within 9 July and 31 Dec 2021. Compared to other baseline and state-of-the-art predictive models, our TCN models yielded the best forecasts, with the lowest mean absolute percentage error (MAPE). Additional experiments, on the IHME COVID-19 data, demonstrate the effectiveness of our TCN models, in comparison with IHME forecasts. Each of our TCN models used a pre-defined set of features; we experimentally validate the effectiveness of these features by showing that these models perform better than other models that instead used other features. Overall, these experimental results demonstrate that our method can accurately forecast hospital resource utilization and pandemic progress for Canada and for each of the six provinces. [ABSTRACT FROM AUTHOR]

Machine learning may improve use of observational data to understand the nutritional epidemiology of cardiovascular disease (CVD) through better modelling of non-linearity, non-additivity, and dietary complexity. Our objective was to develop machine learning prediction models for exploring how nutrients are related to CVD risk and to evaluate their predictive performance. We established a population-based cohort from the Canadian Community Health Survey and measured CVD incidence and mortality from 2004 to 2018 using administrative databases of national hospital discharges and deaths. Predictors included 61 nutrition variables and fourteen socioeconomic, demographic, psychological, and behavioural variables. Conditional inference forest models were interpreted and evaluated by permutation feature importance, accumulated local effects, and predictive discrimination and calibration. A total of 12 130 individuals were included in the study. Use of supplements, caffeine, and alcohol were the most important nutrition variables for prediction of CVD. Supplement use was associated with decreased risk, caffeine was associated with increasing risk, and alcohol had a u-shaped association with risk. The model had an out-of-sample c-statistic of 0.821 (95% confidence interval = 0.801–0.842). Exploratory findings included both known and novel associations and predictive performance was competitive, suggesting that further application of machine learning to nutritional epidemiology may help elucidate risks and improve predictive models. Novelty: Machine learning prediction models were developed for CVD using dietary data. Models were interpreted with interpretable machine learning techniques, revealing diverse associations between diet and CVD. Models achieved comparable or superior predictive performance to existing CVD risk prediction models. [ABSTRACT FROM AUTHOR]

In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.2-s intervals. A long-short term memory network was built using neurosurgeon and student performance in 156 virtually simulated tumor resection tasks. Algorithm predictive ability was tested separately on 144 procedures by scoring the performance of neurosurgical trainees who are at different training stages. The ICEMS successfully differentiated between neurosurgeons, senior trainees, junior trainees, and students. Trainee average performance score correlated with the year of training in neurosurgery. Furthermore, coaching and risk assessment for critical metrics were demonstrated. This work presents a comprehensive technical skill monitoring system with predictive validation throughout surgical residency training, with the ability to detect errors. [ABSTRACT FROM AUTHOR]

This paper investigates how to best compare algorithms for predicting chronic homelessness for the purpose of identifying good candidates for housing programs. Predictive methods can rapidly refer potentially chronic shelter users to housing but also sometimes incorrectly identify individuals who will not become chronic (false positives). We use shelter access histories to demonstrate that these false positives are often still good candidates for housing. Using this approach, we compare a simple threshold method for predicting chronic homelessness to the more complex logistic regression and neural network algorithms. While traditional binary classification performance metrics show that the machine learning algorithms perform better than the threshold technique, an examination of the shelter access histories of the cohorts identified by the three algorithms show that they select groups with very similar characteristics. This has important implications for resource constrained not-for-profit organizations since the threshold technique can be implemented using much simpler information technology infrastructure than the machine learning algorithms. [ABSTRACT FROM AUTHOR]

Study Question: Can we derive adequate models to predict the probability of conception among couples actively trying to conceive?Summary Answer: Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC).What Is Known Already: Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59-64%).Study Design, Size, Duration: Study participants were female, aged 21-45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013-2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry.Participants/materials, Setting, Methods: On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III.Main Results and the Role Of Chance: Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress.Limitations, Reasons For Caution: Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study.Wider Implications Of the Findings: Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work.Study Funding/competing Interest(s): The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications. [ABSTRACT FROM AUTHOR]

Summary: The Fracture Risk Evaluation Model (FREM) identifies individuals at high imminent risk of major osteoporotic fractures. We validated FREM on 74,828 individuals from Manitoba, Canada, and found significant fracture risk stratification for all FREM scores. FREM performed better than age alone but not as well as FRAX® with BMD. Introduction: The FREM is a tool developed from Danish public health registers (hospital diagnoses) to identify individuals over age 45 years at high imminent risk of major osteoporotic fractures (MOF) and hip fracture (HF). In this study, our aim was to examine the ability of FREM to identify individuals at high imminent fracture risk in women and men from Manitoba, Canada. Methods: We used the population-based Manitoba Bone Mineral Density (BMD) Program registry, and identified women and men aged 45 years or older undergoing baseline BMD assessment with 2 years of follow-up data. From linked population-based data sources, we constructed FREM scores using up to 10 years of prior healthcare information. Results: The study population comprised 74,828 subjects, and during the 2 years of observation, 1612 incident MOF and 299 incident HF occurred. We found significant fracture risk stratification for all FREM scores, with AUC estimates of 0.63–0.66 for MOF for both sexes and 0.84 for women and 0.65–0.67 for men for HF. FREM performed better than age alone but not as well as FRAX® with BMD. The inclusion of physician claims data gave slightly better performance than hospitalization data alone. Overall calibration for 1-year MOF prediction was reasonable, but HF prediction was overestimated. Conclusion: In conclusion, the FREM algorithm shows significant fracture risk stratification when applied to an independent clinical population from Manitoba, Canada. Overall calibration for MOF prediction was good, but hip fracture risk was systematically overestimated indicating the need for recalibration. [ABSTRACT FROM AUTHOR]

Unexpected bankruptcies of companies cause significant damage to company partners, investors, employees, countries, commercial and financial creditors. In this context, the financial failure risks should be constantly measured and controlled. However, it is important to constantly check the validity of financial models that measure the risk of the financial failure of companies. Because the higher the validity rates of these models, the higher the probability of investors and researchers to make the right decision according to the results obtained from these models. In this context, validity tests are widely used in the literature, namely Altman (1968), Springate (1978), Ohlson (1980), Fulmer (1984), Zmijewski (1984), Canada (1987) and Grove (2001) models, were applied in this study. The sample of this study consists of 16 companies that operate in the energy sector in the US markets. The universe of the study includes all companies that operate in the energy sector. The research results have shown that the Zmijewski model has been the most successful model for predicting company bankruptcies in the energy sector in the USA. The model that best predicted bankrupt and nonbankrupt companies 1 year and 3 years before the bankruptcy is again the Zmijewski model. However, Fulmer is the best bankruptcy predictor 2 years before the bankruptcy. On the other hand, according to other results of the research, the accuracy rate of Altman, Springate, Canadian, Fulmer and Grover models is well below the average compared to other studies in the literature. [ABSTRACT FROM AUTHOR]

Careful planning of an ambulance service is critical to reduce response times to emergency calls and make assistance more effective. However, the demand for emergency services is highly variable, and good prediction of the number of future emergency calls, and their spatial and temporal distribution, is challenging. In this work, we propose a Bayesian approach to predict the number of emergency calls in future time periods for each zone of the served territory. The number of calls is described by a generalized linear mixed effects model, and inference, in terms of posterior predictive distributions, is obtained through Markov chain Monte Carlo simulation. Our approach is applied in a large city in Canada. The paper demonstrates that using a model for areal data provides good results in terms of predictive accuracy and allows flexibility in accounting for the main features of the dataset. Moreover, it shows the computational efficiency of the approach despite the huge dataset. [ABSTRACT FROM AUTHOR]

Background: Predictive cancer tools focus on survival; none predict severe symptoms. Aim: To develop and validate a model that predicts the risk for having low performance status and severe symptoms in cancer patients. Design: Retrospective, population-based, predictive study Setting/Participants: We linked administrative data from cancer patients from 2008 to 2015 in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). Using the derivation cohort, we developed a multivariable logistic regression model to predict the risk of an outcome at 6 months following diagnosis and recalculated after each of four annual survivor marks. Model performance was assessed using discrimination and calibration plots. Outcomes included low performance status (i.e. 10–30 on Palliative Performance Scale), severe pain, dyspnea, well-being, and depression (i.e. 7–10 on Edmonton Symptom Assessment System). Results: We identified 255,494 cancer patients (57% female; median age of 64; common cancers were breast (24%); and lung (13%)). At diagnosis, the predicted risk of having low performance status, severe pain, well-being, dyspnea, and depression in 6-months is 1%, 3%, 6%, 13%, and 4%, respectively for the reference case (i.e. male, lung cancer, stage I, no symptoms); the corresponding discrimination for each outcome model had high AUCs of 0.807, 0.713, 0.709, 0.790, and 0.723, respectively. Generally these covariates increased the outcome risk by >10% across all models: lung disease, dementia, diabetes; radiation treatment; hospital admission; pain; depression; transitional performance status; issues with appetite; or homecare. Conclusions: The model accurately predicted changing cancer risk for low performance status and severe symptoms over time. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the impact of providing personalized depression risk information on self‐help and help‐seeking behaviors among individuals who are at high risk of having a major depressive episode (MDE). Materials and Methods: In a mixed methods randomized controlled trial, participants who were at high risk of having a MDE, were recruited from across Canada, and were randomized into intervention (n = 358) and control (n = 354) groups. Participants in the intervention group received their personalized depression risk estimated by sex‐specific risk prediction models for MDE. All participants were assessed at baseline, 6 and 12 months. Results: Repeated measure mixed effects modeling showed significant between group differences in self‐help scores. In the complete case analysis, the between group difference in mean self‐help change score was 1.13 at 12 months (effect size = 0.16). Among participants who reported "fair" or "poor health," the between group difference in mean self‐help change score was 2.78 at 12 months (effect size = 0.35). The qualitative data revealed three themes and the findings are consistent with the quantitative results. Conclusions: Providing personalized depression risk information has a positive impact on self‐help in high‐risk individuals, particularly in those with poor health. [ABSTRACT FROM AUTHOR]

Effective conservation requires that species recovery measures are informed by rigorous scientific research. For imperilled freshwater fishes and mussels in Canada, numerous research gaps exist, in part owing to the need for specialized research methods. The Canadian Freshwater Species at Risk Research Network (SARNET) was formed and identified or implemented approaches to address current research gaps, including (1) captive experimental research populations, (2) nonlethal methods for estimating abundance and distribution, (3) nonlethal field methods to measure life-history parameters, (4) species distribution models informed by co-occurring species, (5) conservation physiology to inform habitat and threat science, (6) evidence syntheses to evaluate threats and recovery measures, (7) disease-transmission models to understand mussel–host relationships, (8) experimental mesocosms and manipulative experiments to evaluate key habitat stressors, (9) threat and hazard models for predictive applications, and (10) rigorous evaluation of surrogate species. Over a dozen threat- and recovery-focused SARNET research applications are summarized, demonstrating the value of a coordinated research program between academics and government to advance scientific research on, and to support the recovery of, imperilled freshwater species. [ABSTRACT FROM AUTHOR]

Foot care is a common problem for homeless adults in cities, who often receive fragmented foot care services, resulting in increased foot problems, health complications, hospitalisation, limb removal and disabilities. Among the health factors that negatively affect the homeless, foot health is essential, but often neglected. This study employed a descriptive, cross‐sectional research design to assess the foot care of homeless people and develop recommendations for clinical practice. Using Inlow's 60‐s foot screening method, the foot conditions of a purposive sample of 65 homeless adults were examined in British Columbia, Canada, in 2019–2020. Descriptive and inferential statistics were used to analyse the data. Determinants, such as sex and years of homelessness significantly contributed to homeless individuals' risk of developing foot problems. Differences in perceived and observed foot problems were influenced by equitable access to social health and healthcare services. Foot assessment is not well‐covered by homeless health services and should be implemented as part of the standard medical review of homeless patients. Furthermore, foot assessment should be conducted by healthcare professionals, rather than relying on the standard practice of service users' self‐report. Finally, foot care should be based on tailoring assessments and interventions for the individualised needs of homeless service users. Future research must seek ways to integrate homeless individuals' voices in the development, implementation and evaluation of foot care services. [ABSTRACT FROM AUTHOR]