ResistAI+: A Bayesian change-point causal-machine learning platform for policy-ready antimicrobial resistance stewardship and surveillance

We are excited to express our interest in participating in the 2025 AMR Data Challenge, building on our 2024 ResistAI platform. By leveraging a robust data foundation—including Pfizer ATLAS, Paratek KEYSTONE, and EARS surveillance datasets—we aim to integrate high-impact innovations that transform antimicrobial resistance (AMR) data into actionable insights across clinical, stewardship, public health, and health system levels.

1. Helping Improve Patient Outcomes

We will integrate Explainable AI (XAI) into our predictive models using SHAP values, enabling clinicians to understand the specific factors—such as geography, age, or drug usage—that drive a resistance prediction. This transparency empowers evidence-based prescribing decisions, reduces treatment failure, and tailors care to patient subgroups at greatest risk of resistance.

2. Strengthening Antimicrobial Stewardship

Our enhanced platform will include change-point detection algorithms (via Python’s ruptures library) to identify sudden shifts in resistance trends. These early alerts allow hospitals and stewardship teams to adjust formularies or prescribing behavior proactively—before resistance becomes entrenched.

3. Informing Public Health Practice

By stratifying resistance forecasts by region, age group, and pathogen, and highlighting temporal changes, we provide public health stakeholders with timely insights into emerging AMR threats. These tools can support surveillance programs, prioritize interventions, and align with WHO’s goals for early-warning AMR systems.

4. Strengthening Health Systems

Our user-friendly, web-based platform democratizes access to advanced analytics, even in low-resource settings. Scalable, interpretable, and responsive, it offers health systems a powerful decision-support tool that integrates forecasting, explainability, and surveillance into one accessible interface

Through these innovations, we aim to not just predict AMR—but to help understand, act, and intervene in meaningful ways. We look forward to contributing significantly to the 2025 challenge and supporting global efforts to combat AMR.