Agent-based models of emergency departments generate synthetic EHR data to test whether machine learning models for length-of-stay prediction lose performance under mass casualty incident conditions.
FUTURE-AI : International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
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Human-computer interactions encode mental health states with high accuracy, as shown by the MAILA framework trained on 18,200 recordings and 1.3 million self-reports.
TRACE is a metrologically-grounded four-layer engineering framework for trustworthy agentic AI that enforces an ML-LLM split, stateful policies, human supervision, and a parsimony metric across critical domains.
citing papers explorer
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Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents
Agent-based models of emergency departments generate synthetic EHR data to test whether machine learning models for length-of-stay prediction lose performance under mass casualty incident conditions.
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Human-computer interactions predict mental health
Human-computer interactions encode mental health states with high accuracy, as shown by the MAILA framework trained on 18,200 recordings and 1.3 million self-reports.
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TRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domains
TRACE is a metrologically-grounded four-layer engineering framework for trustworthy agentic AI that enforces an ML-LLM split, stateful policies, human supervision, and a parsimony metric across critical domains.