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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
Intelligence This project develops an AI-informed, community-grounded framework to understand and mitigate antimicrobial resistance (AMR) risks within a planetary health context. Building on the risk
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of AMR infections. The specific aims of this project are: 1. Rapidly identify AMR and predict treatment responses through use of genomics and machine learning in a clinical context. 2. Detect
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learning in a clinical context Detect healthcare-associated transmission of AMR in real-time and transform outbreak response through use of novel long-read sequencing Use predictive approaches to
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learning in a clinical context Detect healthcare-associated transmission of AMR in real-time and transform outbreak response through use of novel long-read sequencing Use predictive approaches to
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enhance the code quality. Multi-modal models can also be explored which can take into account additional context for better decision making. Overall, new techniques will be developed such that the
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enhance the code quality. Multi-modal models can also be explored which can take into account additional context for better decision making. Overall, new techniques will be developed such that the