Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand

The growing acknowledgment of population wellbeing as a key indicator of societal prosperity has propelled governments worldwide to devise policies aimed at improving their citizens' overall wellbeing. In New Zealand, the General Social Survey provides wellbeing metrics for a representative subset of the population (~ 10,000 individuals). However, this sample size only provides a surface-level understanding of the country's wellbeing landscape, limiting our ability to comprehensively assess the impacts of governmental policies, particularly on smaller subgroups who may be of high policy interest. To overcome this challenge, comprehensive population-level wellbeing data is imperative. Leveraging New Zealand's Integrated Data Infrastructure, this study developed and validated the efficacy of three predictive models-Stepwise Linear Regression, Elastic Net Regression, and Random Forest-for predicting subjective wellbeing outcomes (life satisfaction, life worthwhileness, family wellbeing, and mental wellbeing) using census-level administrative variables as predictors. Our results demonstrated the Random Forest model's effectiveness in predicting subjective wellbeing, reflected in low RMSE values (~ 1.5). Nonetheless, the models exhibited low R2 values, suggesting limited explanatory capacity for the nuanced variability in outcome variables. While achieving reasonable predictive accuracy, our findings underscore the necessity for further model refinements to enhance the prediction of subjective wellbeing outcomes.

Personal Location as Health-Related Data: Public Knowledge, Public Concern, and Personal Action

OBJECTIVES

Personal health information (PHI), including health status and behaviors, are often associated with personal locations. Smart devices and other technologies routinely collect personal location. Therefore, technologies collecting personal location do not just create generic questions of privacy, but specific concerns related to PHI.

METHODS

To assess public opinion on the relationship between health, personal location, and privacy, a national survey of US residents was administered online in March 2020. Respondents answered questions about their use of smart devices and knowledge of location tracking. They also identified which of the locations they could visit were most private and how to balance possibilities that locations may be private but can also be useful to share.

RESULTS

Of respondents that used smart devices (n = 688), a majority (71.1%) indicated they knew they had applications tracking their location, with respondents who were younger (P < .001) and male (P = .002) and with more education (P = .045) more likely to indicate "yes." When all respondents (N = 828) identified the locations on a hypothetical map they felt were most private, health-related locations (substance use treatment center, hospital, urgent care) were the most selected.

CONCLUSIONS

The historical notion of PHI is no longer adequate and the public need greater education on how data from smart devices may be used to predict health status and behaviors. The COVID-19 pandemic brought increased attention to personal location as a tool for public health. Given healthcare's dependence upon trust, the field needs to lead the conversation and be viewed as protecting privacy while usefully leveraging location data.

Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences

Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.

A novel approach of creating sustainable urban planning solutions that optimise the local air quality and environmental equity in Helsinki, Finland: The CouSCOUS study protocol

Background

Air pollution is one of the major environmental challenges cities worldwide face today. Planning healthy environments for all future populations, whilst considering the ongoing demand for urbanisation and provisions needed to combat climate change, remains a difficult task.

Objective

To combine artificial intelligence (AI), atmospheric and social sciences to provide urban planning solutions that optimise local air quality by applying novel methods and taking into consideration population structures and traffic flows.

Methods

We will use high-resolution spatial data and linked electronic population cohort for Helsinki Metropolitan Area (Finland) to model (a) population dynamics and urban inequality related to air pollution; (b) detailed aerosol dynamics, aerosol and gas-phase chemistry together with detailed flow characteristics; (c) high-resolution traffic flow addressing dynamical changes at the city environment, such as accidents, construction work and unexpected congestion. Finally, we will fuse the information resulting from these models into an optimal city planning model balancing air quality, comfort, accessibility and travelling efficiency.

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison

Background

Uric acid is associated with noncommunicable diseases such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of noncommunicable diseases. Most studies on uric acid have been performed in developed countries, and the application of machine-learning approaches in uric acid prediction in developing countries is rare. Different machine-learning algorithms will work differently on different types of data in various diseases; therefore, a different investigation is needed for different types of data to identify the most accurate algorithms. Specifically, no study has yet focused on the urban corporate population in Bangladesh, despite the high risk of developing noncommunicable diseases for this population.

Objective

The aim of this study was to develop a model for predicting blood uric acid values based on basic health checkup test results, dietary information, and sociodemographic characteristics using machine-learning algorithms. The prediction of health checkup test measurements can be very helpful to reduce health management costs.

Methods

Various machine-learning approaches were used in this study because clinical input data are not completely independent and exhibit complex interactions. Conventional statistical models have limitations to consider these complex interactions, whereas machine learning can consider all possible interactions among input data. We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. We evaluated the performance of these five widely used machine-learning models using data collected from 271 employees in the Grameen Bank complex of Dhaka, Bangladesh.

Results

The mean uric acid level was 6.63 mg/dL, indicating a borderline result for the majority of the sample (normal range <7.0 mg/dL). Therefore, these individuals should be monitoring their uric acid regularly. The boosted decision tree regression model showed the best performance among the models tested based on the root mean squared error of 0.03, which is also better than that of any previously reported model.

Conclusions

A uric acid prediction model was developed based on personal characteristics, dietary information, and some basic health checkup measurements. This model will be useful for improving awareness among high-risk individuals and populations, which can help to save medical costs. A future study could include additional features (eg, work stress, daily physical activity, alcohol intake, eating red meat) in improving prediction.

Use of artificial intelligence in infectious diseases

Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. These pathogens are transmitted, directly or indirectly, and can lead to epidemics or even pandemics. The resulting infection may lead to mild-to-severe symptoms such as life-threatening fever or diarrhea. Infectious diseases may be asymptomatic in some individuals but may lead to disastrous effects in others. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. With the advent of mathematical tools, scientists are now able to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for their ability to better diagnose certain types of cancer from imaging data. This chapter aims at identifying potential applications of machine learning in the field of infectious diseases. We are deliberately focusing on key aspects of infection: diagnosis, transmission, response to treatment, and resistance. We are proposing the use of extreme values as an avenue of interest for future developments in the field of infectious diseases. This chapter covers a series of applications selectively chosen to showcase how artificial intelligence is moving the field of infectious disease further and how it helps institutions to better tackles them, especially in low-income countries.

Predicting nationwide obesity from food sales using machine learning

The obesity epidemic progresses everywhere across the globe, and implementing frequent nationwide surveys to measure the percentage of obese population is costly. Conversely, country-level food sales information can be accessed inexpensively through different suppliers on a regular basis. This study applies a methodology to predict obesity prevalence at the country-level based on national sales of a small subset of food and beverage categories. Three machine learning algorithms for nonlinear regression were implemented using purchase and obesity prevalence data from 79 countries: support vector machines, random forests and extreme gradient boosting. The proposed method was validated in terms of both the absolute prediction error and the proportion of countries for which the obesity prevalence was predicted satisfactorily. We found that the most-relevant food category to predict obesity is baked goods and flours, followed by cheese and carbonated drinks.