How do you use machine learning and artificial intelligence in environmental health research?

2 Health outcome prediction

Health outcome prediction is the process of estimating the probability or severity of a health condition or event, such as asthma, cardiovascular disease, or mortality, based on exposure and other factors. ML/AI can help environmental health researchers improve health outcome prediction by using various types of data, such as clinical records, biomarkers, genetic information, and environmental indicators, and by applying different models, such as regression, classification, or survival analysis, to identify associations and causal relationships between the data. For example, ML/AI can be used to predict the risk of hospitalization or death due to COVID-19, based on exposure to air pollution, demographic characteristics, comorbidities, and behavioral factors.

3 Risk assessment

Risk assessment is the process of evaluating the potential adverse effects of an environmental agent on human health and well-being, and the uncertainty associated with the evaluation. ML/AI can help environmental health researchers improve risk assessment by incorporating multiple sources of evidence, such as epidemiological studies, toxicological experiments, and mechanistic models, and by applying methods, such as meta-analysis, Bayesian inference, or causal inference, to synthesize the evidence and quantify the uncertainty. For example, ML/AI can be used to assess the risk of cancer from exposure to arsenic in drinking water, based on a combination of human and animal data, and to estimate the impact of different exposure scenarios and interventions.

4 Intervention evaluation

Intervention evaluation is the process of measuring the effectiveness and impact of an action or policy that aims to reduce or prevent exposure to an environmental agent or to improve health outcomes. ML/AI can help environmental health researchers improve intervention evaluation by using various types of data, such as experimental, observational, or simulated data, and by applying techniques, such as randomized controlled trials, quasi-experimental designs, or counterfactual analysis, to estimate the causal effects of the intervention and to account for confounding and selection bias. For example, ML/AI can be used to evaluate the impact of a low-emission zone on air quality and health outcomes, based on before-and-after measurements of pollution levels and health indicators, and by comparing with a control group or a synthetic control group.