Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot.

Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables' relationships with the outcome.

Who is most at risk of dying if infected with SARS-CoV-2? A mortality risk factor analysis using machine learning of patients with COVID-19 over time: a large population-based cohort study in Mexico.

Objective: COVID-19 would kill fewer people if health programmes can predict who is at higher risk of mortality because resources can be targeted to protect those people from infection. We predict mortality in a very large population in Mexico with machine learning using demographic variables and pre-existing conditions.

Design: Cohort study.

Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study.

Background: Gestational diabetes (GDM) is prevalent and benefits from timely and effective treatment, given the short window to impact glycemic control. Clinicians face major barriers to choosing effectively among treatment modalities [medical nutrition therapy (MNT) with or without pharmacologic treatment (antidiabetic oral agents and/or insulin)]. We investigated whether clinical data at varied stages of pregnancy can predict GDM treatment modality.

Associations of household food insufficiency with childhood depression and anxiety: a nationwide cross-sectional study in the USA.

Objective: Household food insufficiency (HFIS) is a major public health threat to children. Children may be particularly vulnerable to HFIS as a psychological stressor due to their rapid growth and accelerated behavioural and cognitive states, whereas data focusing on HFIS and childhood mental disorders are as-yet sparse. We aimed to examine the associations of HFIS with depression and anxiety in US children.

Protocol for audit of current Filipino practice in rehabilitation of stroke inpatients.

Background: Stroke is one of the leading medical conditions in the Philippines. Over 500,000 Filipinos suffer from stroke annually. Provision of evidence-based medical and rehabilitation management for stroke patients has been a challenge due to existing environmental, social, and local health system issues.